Method of predicting and processing image fine structures

ABSTRACT

By simulating the interference-generated image fine structures with a continuous-tone printer or a display, the image fine structures can be predicted as faithfully as can be achieved by high quality machines. This is done without sacrificing the advantages of the continuous-tone printer or display (i.e., convenience in handling and the low costs of materials and machine), while ensuring that moiré, line discontinuities, imperfections in straight lines and other image fine structures that may appear in a printed document are predicted on a hard or soft proof accurately and conveniently and within a short time. The intensity of moiré and other peculiar patterns that occur when simulating the image fine structures in a printed document can be freely adjusted in accordance with the type of the printing machine which is to be employed to produce the printed document. Pixel values of an original image are separated, processed, and subjected to weighted averaging with adjustable weights.

BACKGROUND ART

This invention relates to a method of predicting and processing imagefine structures. More particularly, the invention relates to a method ofpredicting and processing image fine structures which, when applied to asystem for preparing color proofs with an image output device such as acolor printer or a CRT display before a printed color document carryinghalftone dot images (or simply “halftone images”) is actually producedwith a color printing machine using rotary presses or the like that havepress plates mounted thereon, can ensure that image fine structures suchas moiré and a rosette image which would occur in the actually producedprinted document are represented in advance either as an image on thedisplay such as CRT (the image is generally referred to as “soft proof”)or as an image on a hard copy output from the color printer (which isgenerally referred to as “hard proof”).

The process of producing printed documents carrying halftone images witha color printing machine using rotary presses or the like is not onlytime-consuming but also costly and, hence, it is common practice toproduce proofs with a device other than the color printing machine whichis commonly referred to as a proofer before the printed document isoutput as the actual product. The production of proofs with a prooferhas two purposes, one for predicting the colors to be reproduced on theprinted document (this may be called “simulation of color reproduction”)and the other is for predicting the image fine structures to be producedon the printed document (which may be called “simulation of image finestructures”).

Two types of proofers have heretofore been proposed for use in theproduction of proofs for printed documents, one being a proofer whichinvolves dot formation and the other being a non-dot forming proofer.Proofing technology, or the techniques for predicting and processingimage fine structures using proofers, is conventionally implemented bythe following three methods. In the first method, a high-resolutiondigital printer is used as a dot-forming proofer and halftone images areactually produced. This technique has the advantage of correctlysimulating the image fine structures which will appear on the printeddocument.

A dot-forming proofer is also used in the second method and halftoneimages (pictures) formed on printing lith films for four plates of C(cyan), M (magenta), Y (yellow) and K (black) are individuallytransferred to special chemical materials by exposure and the resultingfour sheets of chemical materials for the C, M, Y and K plates which arecommonly referred to as “transfer films” are placed one on another. Anexample of such proofers is one that employs the transfer films marketedby the Applicant. In this method, transfer films for the four plates ofC (cyan), M (magenta), Y (yellow) and K (black) are prepared by dotformation using the actual screen ruling and transferred onto a printsheet at the actual screen angles to thereby produce a hard proof. Thisapproach also has the advantage of correctly simulating the image finestructures which will appear on the printed document.

The third method uses a non-dot forming proofer which is exemplified bya system employing a continuous-tone color printer such as one whichuses sublimable dye pigmented inks and which operates on a densitymodulation process to achieve a resolution of 300 dpi. This type ofcolor printer represents the original image as a continuous-tone imagewithout forming dots and, hence, has the advantage of producing proofsby simple procedures.

Of the two methods that use a dot forming proofer, the first approachwhich actually produces halftone dots with a high-resolution digitalprinter allows for halftone representation and is capable of correctlysimulating the image fine structures which will appear on the printeddocuments. On the other hand, the high-resolution digital printer is anexpensive device and requires high running cost; hence, the first methodis not a convenient way to be performed in practice.

The second approach which superposes four transfer films for the platesof CMYK which are made of special chemical materials also allows forhalftone representation and is capable of correctly simulating the imagefine structures which will appear on the printed document. However, theapparatus used in the method is also costly. In addition, the cost ofthe print sheet is relatively high and the transfer films made ofspecial chemical materials are also expensive. What is more, the processup to the stage of proof production is cumbersome (i.e., requires muchlabor due to manual operations) and, hence, a comparatively long time istaken to produce the desired proof; in other words, the second method isnot necessarily an easy way to operate. In addition, it has beentheoretically difficult to achieve color matching with the ultimateprinted document.

In the third method which uses a non-dot forming proofer, the originalimage is represented as a continuous-tone image by means of acontinuous-tone printer without producing dots. Therefore, this methodis inexpensive, is convenient and can produce a proof within a shorttime. On the other hand, the method gives no consideration torepresentation by dots and is not capable of representing halftones;hence, the proof produced by the method can be used in simulating colorreproduction but not in simulating image fine structures.

Under the circumstances, there has been a strong need for a technologythat retains the advantage of low cost and convenience of the thirdmethod using a non-dot forming proofer and which yet is capable ofrepresenting dot-generated image fine structures as in the first andsecond methods which employ a dot-forming proofer.

Further referring to the third approach which uses a non-dot formingproofer, it has heretofore been customary to produce color proofs forexamining and correcting colors and so forth before a printed colordocument carrying halftone images is ultimately produced by a colorprinting machine. The proofs are produced using a color printer thatforms an image for each pixel by a density gradation process (alsocalled “continuous gradation process”) and this is primarily because thecolor printer is of a comparatively simple composition and inexpensive.In addition, by means of the color printer, a hard copy having an imageformed on a sheet can easily be produced a plurality of times within ashort period of time since, as is well known in the art, the preparationof process-plate films and press plates which are required by colorprinting machines are not needed by the color printer.

FIG. 23 shows the flow of a conventional process for producing colorproofs using a color printer. First, the image on an image document 52is read two-dimensionally with an image reader such as a color scannerhaving a CCD linear image sensor or the like and gradation(continuous-tone) image data Ia for each of the colors R (red), G(green) and B (blue) are generated (step S51: image reading step).

Then, in step S52, the RGB gradation image data Ia are rendered by acolor conversion process using conversion lookup tables or the like intodot area percentage data (also referred to as “dot percentage data” or“original image pixel dot percentage data”) aj for the four plates ofrespective colors C (cyan), M (magenta), Y (yellow) and K (black), wherej=0, 1, 2, 3 (0 represents the color C, 1 the color M, 2 the color Y,and 3 the color K). The color conversion process allows for variousversions in relation to the color printing machine to be described lateron and it is usually the proprietary know-how of individual printingcompanies who employ different color printing machines.

Halftone images are produced by the color printing machine and, hence,in order to produce a printed color document in the actual practice, thedot area percentage data aj produced by the color conversion process arerendered into bit map data, on the basis of which a process-plate filmor the like is generated. A problem with the color printing machine isthe need to use an automatic image developing machine, so the processfollowing the generation of the process-plate film is considerablycomplicated. To facilitate the production of color proofs, a colorprinter 53 (which may hereinafter be referred to as either “colordigital printer” or “DP” as the case may be) is employed for the reasonsset forth above. DP 53 forms an image on a donor film by a densitygradation process in which the intensity and time of emission of threeprimary colors from an LED (light-emitting diode) or a laser aredigitally controlled pixel for pixel and the image is transferred to animage-receiving sheet, whereby image formation is effected on the sheet.Compared to the color printing machine which generates presensitizedplates from printing plates and which produces a printed color documentusing the presensitized plates, DP 53 is considerably inexpensive. Inaddition, it is smaller in volume and lighter in weight.

In order to employ DP 53, it is necessary that the halftone-dot areapercentage data aj of the four CMYK plates produced in step S52 beconverted into image data (also called “common color space data”) whichare independent of devices including a printing device, a CRT, aphotographic device, an LED, etc. and which are exemplified bytristimulus value data X, Y, Z. To meet this need, the halftone-dot areapercentage data aj of the four CMYK plates are converted intotristimulus value data X, Y, Z in an image data processing section (stepS54). The image data processing is conventionally carried out using theNeugebauer's equation.

Prior to step S54, colorimetric data Xi, Yi, Zi (i represents 2⁴=16colors for the four CMYK plates and ranges from 0 to 15) for the colorsof printing inks are measured with a colorimeter (step S53). To measurethe colorimetric data Xi, Yi, Zi, the 16 colors are printed on a printsheet which will be used to produce a printed color document with acolor printing machine, thereby preparing “color patches”. This processis commonly referred to as “solid printing”. The 16 colors correspond tothe presence and absence of the respective colors, C, M, Y, K (2⁴=16).

Specifically, the 16 colors consist of color W (white) which representsthe background color of the print sheet when nothing is printed on it,the primary colors C, M, Y, color K (black), and mixed colors C+M, C+Y,C+K, M+Y, M+K, Y+K, C+M+Y, C+M+K, C+Y+K, M+Y+K, and C+M+Y+K. These 16colors are also called “16 basic colors”. The colors of reflection fromthe colors printed on the print sheet are measured with a colorimetersuch as a spectrometer to produce the colorimetric data Xi, Yi, Zi.

In the image data processing using the Neugebauer's equation, thecolorimetric data Xi, Yi, Zi are multiplied by the area percentage databi (i=0-15) as a coefficient [(see the following equations (6)] toproduce the tristimulus value data X, Y, Z which have been subjected toimage data processing (step S54):

X=Σ _(i=0) ¹⁵ bi·Xi

Y=Σ _(i=0) ¹⁵ bi·Yi

Z=Σ _(i=0) ¹⁵ bi·Zi  (6)

The area percentage data bi of the 16 basic colors which are included asa coefficient in equations (6) are determined from the halftone-dot areapercentage data aj by performing probability calculations as follows:

b0=(1−c)(1−m)(1−y)(1−k)

b1=c·(1−m)(1−y)(1−k)

b2=(1−c)·m·(1−y)(1−k)

b3=c·m(1−y)(1−k)

b4=(1−c)(1−m)·y·(1−k)

b5=c·(1−m)·y·(1−k)

b6=(1−c)·m·y·(1−k)

b7=c·m·y·(1−k)

b8=(1−c)(1−m)(1−y)·k

b9=c·(1−m)(1−y)·k

b10=(1−c)·m·(1−y)·k

b11=c·m·(1−y)·k

b12=(1−c)(1−m)·y·k

b13=c·(1−m)·y·k

b14=(1−c)·m·y·k

b15=c·m·y·k  (7)

To provide for easy understanding by intuition, the halftone-dot areapercentage data aj (j=0-3) are set to a0=c, a1=m, a2=y and a3=k in theabove equations (7) and c, m, y and k represent the halftone-dot areapercentage data of the respective color plates C, M, Y and K. Take, forexample, b3 which represents the area percentage of the mixed color C+Min the equations (7); this parameter can be determined by multiplying c(the probability that plate C exists), m (the probability that plate Mexists), 1−y (the probability that plate Y does not exist), and 1−k (theprobability that plate K does not exist). Therefore, the Neugebauer'sequation expressed by the equations (7) can be understood as being basedon the theory of probability.

The tristimulus value data X, Y, Z thus obtained by image dataprocessing according to equations (6) are supplied to DP 53, in whichthey are converted into data for the three primaries with respect to thelaser beam or the like on the basis of lookup tables (LUTs). Said dataare so-called “device dependent image data”, which are sometimesreferred to as “inherent color space data”. Thereafter, DP 53 generatesa color proof CPa which is a hard copy having an image formed on a sheet(step S54).

When the tristimulus value data X, Y, Z for DP 53 are generated usingthe Neugebauer's equation as described above, the colors of the printedcolor document to be produced with a color printing machine can bereproduced faithfully in the image on the hard copy due to the use ofthe colorimetric data obtained by measurement with a colorimeter asrepresenting the colors of the image to be formed on the printed colordocument. On the other hand, image fine structures which will appear onthe printed color document, such as moiré, a rosette image and otherpeculiar patterns caused by interference fringes cannot be reproduced inthe image on the hard copy.

If image fine structures are to appear on the printed color document,they should also be reproduced faithfully on the color proof CPa whichis output from DP 53. In this respect, the conventional color proof CPawhich fails to reproduce image fine structures is not an accurate(faithful) proof for the printed color document.

The reason for the failure of image fine structures to appear on thehard copy from DP 53 is conceivably because the Neugebauer's equation isbased on the theory of probability as described above.

Under the circumstances, the present inventors made intensive studies inorder to verify the hypothesis that if pixel data which compose inputimage data for use with a color printer are generated without relyingupon the Neugebauer's equation, image fine structures such as moiré, arosette image and so forth which are peculiar to the printed document tobe produced can be reproduced on a color proof in an accurate andfaithful manner. As a result, the inventors proposed in UnexaminedPublished Japanese Patent Application (kokai) No. Hei 8-192540 atechnique which is capable of faithful reproduction of not only thecolors of a printed image but also the image fine structures such asmoiré and a rosette image which appear due to halftoning.

This technique enables the simulation of interference-generated imagefine structures using a continuous-tone printer but at the same time itsuffers from the problem of taking time in processing. The reason forthe slow processing speed of this technique is the great number ofmathematical operations to be performed on individual pixels, which inturn is caused by the need to simulate halftone dots by performing thesame calculations for each pixel as are effected in the halftoning stepin the printing process.

SUMMARY OF THE INVENTION

The present invention has been accomplished under these circumstancesand its first principal object is to provide a method of predicting andprocessing image fine structures which, by simulating theinterference-generated image fine structures with a continuous-toneprinter or a display, enables the image fine structures to be predictedas faithfully as can be achieved by high quality machines withoutsacrificing the advantages of the continuous-tone printer or display(i.e., convenience in handling and the low costs of materials andmachine), thus ensuring that moiré, line discontinuities, imperfectionsin straight lines and other image fine structures that will appear in aprinted document can be predicted on a hard or soft proof in an accurateand convenient way within a short time, and which also ensures that theintensity of moiré and other peculiar patterns that occur whensimulating the image fine structures in a printed document can be freelyadjusted in accordance with the type of the printing machine which is tobe employed to produce the printed document.

Another object of the invention which is associated with theabove-stated first primary object is to provide a method of predictingand processing image fine structures which is capable of predictinginterference-generated image fine structures on a hard or soft proof inan accurate and convenient way without impairing the sharpness of aprinted document or by ensuring that the intensity of sharpness asachieved when simulating the image fine structures in the printeddocument can be freely adjusted in accordance with the type of theprinting machine which is to be employed to produce the printeddocument.

Yet another object of the invention which is associated with the statedtwo objects is to provide a method of predicting and processing imagefine structures which can also achieve faithful reproduction of thecolors of a printed document simultaneously with the prediction of imagefine structures.

The term “line discontinuities” as used herein means such an image finestructure that a tall object which will be approximately represented byone pixel on a printed document, for example, a vertical flagpolelocated at a far distance appears to be discontinued periodically.

The term “imperfections in straight lines” means such an image finestructure that a long object which will be approximately represented byone pixel on a printed document, for example, a horizontal handrail onthe roof of a far distant building appears to be non-straightperiodically.

The second principal object of the invention is to provide a method ofpredicting and processing image fine structures which uses a colorprinter or a like image output device that is comparatively low in costand resolution to ensure that moiré, a rosette image and other imagefine structures that will appear on a printed color document of highresolution due to halftone dots can be predicted and reproduced asfaithfully as can be achieved by high quality machines withoutsacrificing the advantages of the image output device (i.e., conveniencein handling and the low cost of materials and machine) and which iscapable of rapid simulation (prediction and processing) of the imagefine structures.

Another object of the invention which is associated with theabove-stated second primary object is to provide a method of predictingand processing image fine structures which can also achieve faithfulreproduction of the colors of a printed document simultaneously with thesimulation of interference-caused image fine structures.

Yet another object of the invention which is associated with the statedtwo objects is to provide a method of predicting and processing imagefine structures which is capable of increasing the precision insimulation of the colors of a printed document and/or adjusting thedegree of contrast of the simulated moiré, rosette image or other imagefine structures in accordance with the printing machine to be eventuallyused and the appearance of the printed document to be produced.

In order to achieve the above-said first principal object of the presentinvention, there is provided a method of predicting and processing imagefine structures, in which the pixel values of the original imageseparated into pixels for each of the CMY or CMYK plates are convertedto pixel values for predicting image fine structures which will appearin a printed halftone image, characterized in that the values of thepixel to be converted and neighboring pixels are subjected to weightedaveraging with adjustable weights dependent on the period of grid unitsthat is determined by the screen ruling and screen angle for saidprinted halftone image.

In order to achieve the above-said second principal object of thepresent invention, there is provided a method of predicting andprocessing image fine structures, in which the original image isconverted to dot area percentage data for n plates including at leastthree primary colors and, for ensuring that a color proof for a colorprinted document which is to be produced with a color printing machineis output from an image output device using said dot area percentagedata, the latter are converted to gradation image data for predictingthe image fine structures on said color printed document, characterizedin that 0-100% dot area percentage data for each of the n plates arepreliminarily divided into N stages and said gradation image data forpredicting said image fine structures are determined for each of themonochromatic images for a total of N^(n) colors, thereby constructing alookup table that has the repeating pixel size necessary for predictingsaid image fine structures and which has a position parameter and ncolor parameters as arguments, the dot area percentage data for the nplates associated with said original image being converted to saidgradation image data by referencing said lookup table.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an exemplary configuration of an imagefine structure predicting system which incorporates the first embodimentof the invention method of predicting and processing image finestructures;

FIG. 2 is a diagram illustrating an example of the basic flow of theinvention method of predicting and processing image fine structures;

FIG. 3A is a schematic representation of an original image consisting ofpixels divided to 400 dpi, to which reference may be had in explainingthe operating theory of the invention method of predicting andprocessing image fine structures;

FIG. 3B is a schematic representation of a screen grid having a screenruling of 175 and a screen angle of 45 degrees;

FIG. 3C is a schematic representation of the screen grid of FIG. 3Bplaced on the original image of FIG. 3A;

FIG. 4A is a schematic representation of an original image which may besubjected to the step of weighted averaging in the invention method ofpredicting and processing image fine structures;

FIG. 4B is a schematic representation to which reference may be had inexplaining the generation of a 3×3 weighting matrix from a rationalnumber screen template of high resolution;

FIG. 5A is a schematic representation of a screen grid having a screenruling of 175 and a screen angle of 45 degrees, which may be used in thestep of generating a weight function in the invention method ofpredicting and processing image fine structures;

FIG. 5B is a schematic representation of the profile of a weightfunction drawn on FIG. 5A;

FIG. 6 is a flow chart to which reference may be had in explaining thegeneration of various weighting matrices in the invention method ofpredicting and processing image fine structures;

FIG. 7 is a diagram showing part of a rational number screen template(threshold matrix) of 2000 dpi which may be used in the invention methodof predicting and processing image fine structures;

FIG. 8 is a diagram showing an exemplary weighting matrix of 400 dpiwhich may be used in the invention method of predicting and processingimage fine structures;

FIG. 9 is a diagram showing a specific example of the weighted averagingstep in the invention method of predicting and processing image finestructures;

FIG. 10 is a diagram showing a threshold matrix of 2000 dpi which isgenerated by emphasizing the threshold values of the threshold matrixshown in FIG. 7;

FIG. 11 is a diagram showing an exemplary weight matrix of 400 dpi thatis generated by emphasis of threshold values and which is to be used inthe invention method of predicting and processing image fine structures;

FIG. 12 is a diagram showing the composition of a 3×3 Gaussian filterwhich is to be used in the invention method of predicting and processingimage fine structures;

FIG. 13 is a diagram showing the composition of a 15×15 Gaussian filterwhich is also to be used in the invention method of predicting andprocessing image fine structures;

FIG. 14A is a diagram showing an exemplary composition of a sharpnessfilter which is a reverse filter having opposite characteristics tothose of the Gaussian filter used in the invention method of predictingand processing image fine structures;

FIG. 14B is a diagram showing an exemplary composition of a sharpnessfilter having stronger characteristics than the sharpness filter of FIG.14A;

FIG. 14C is a diagram showing an exemplary composition of a sharpnessfilter having stronger characteristics than the sharpness filter of FIG.14B;

FIG. 15 is a flow chart to which reference may be had in explaining howthe invention method of predicting and processing image fine structuresis implemented in the use of the image fine structure predicting system;

FIG. 16 is a flow chart showing a system of producing color proofs byimplementing the second embodiment of the invention method of predictingand processing image fine structures, as well as the basic flow of acolor printing system;

FIG. 17 is a diagram to which reference may be had in explaining theprocess of generating bit map data in the color printing system shown inFIG. 16;

FIG. 18 is a flow chart showing the basic flow of an exemplary processof generating a six-dimensional lookup table in the system of producingcolor proofs which is shown in FIG. 16;

FIG. 19 is a diagram showing an exemplary composition of ananti-aliasing filter which is to be used in the process of generating asix-dimensional lookup table as shown in FIG. 18;

FIG. 20 is a diagram showing the frequency response of the anti-aliasingfilter shown in FIG. 19;

FIG. 21A is a diagram showing exemplary bit map data of 28×28 dots for Cplate, to which reference may be had in explaining how averagecolorimetric data are generated from bit map data of comparatively highresolution in the process of generating a six-dimensional lookup tableas shown in FIG. 18;

FIG. 21B is a diagram showing exemplary bit map data of 28×28 dots for Mplate;

FIG. 22A is a diagram showing the first processing step of averagecolorimetric data with an anti-aliasing filter in the process ofgenerating a six-dimensional lookup table as shown in FIG. 18;

FIG. 22B is a diagram showing the next processing step; and

FIG. 23 is a flow chart of a conventional system of producing colorproofs.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The method of the invention for predicting and processing image finestructures is hereunder described in detail with reference to thepreferred embodiments shown in FIGS. 1-22 of the accompanying drawings.To begin with, the method according to the first embodiment of theinvention will be described with reference to FIGS. 1-15.

FIG. 1 shows the general layout of a system for predicting andprocessing image fine structures by implementing the first embodiment ofthe invention method of predicting and processing image fine structures.The system generally indicated by 11 in FIG. 1 has a computer 12 as anengine for performing the prediction and processing of image finestructures. An image input device 13, a screen attribute input device14, a moiré intensity input device 15 and a sharpness intensity inputdevice 16 are connected to the input side of the computer 12 whereas animage output device 19 having a printer 17 and/or a display 18 isconnected to the output side of the computer 12.

The computer 12 may be of any type that is capable of executing softwarefor the prediction and processing of image fine structures and commonpersonal computers or workstations can be employed that permit theinstallation and execution of software other than that dedicated to theprediction and processing of image fine structures.

The image input device 13 is typically a printing scanner (image readingmeans) or an external storage device which is a component of theabove-mentioned workstation or the like. The image input device 13outputs imagewise separated data for the original image (i.e., for theCMYK plates), which is passed through a resolution converting block 21in the computer 12 and the resulting original image data I (for the CMYKplates, consisting of the original image data C for C plate, originalimage data M for M plate, original image data Y for Y plate, andoriginal image data K for K plate) are supplied into an image finestructure predicting and processing block (or means) 22.

The resolution converting block 21, image fine structure predicting andprocessing block 22 and other components of the computer 12 which arelabelled with “block” and shown in FIG. 1 are generally intended toperform processing with software.

The original image data delivered from the image input device 13 aregradation image data represented in CMYK colors and provide images ofvarious resolutions that may be represented by an image format (e.g.TIFF) capable of storing attributes such as the number of bits in whichthe data are expressed, the image size and the image resolution. Suchoriginal image data of various resolutions are converted by the block 21into data having the resolution to be achieved by printer 17, namely,the output resolution which is typically 400 dpi, and the resultingoriginal image data I of a multilevel gradation, say, 8-bit gradationare supplied into the block 22. The resolution conversion to beperformed in the block 21 is typically executed by a well-known areainterpolating process and, hence, there will be no occurrence of imagefine structures or color changes.

The screen attribute input device 14 is composed of a keyboard, a mouseand so forth and used to supply the image fine structure predicting andprocessing block 22 with the screen ruling LPI (lines per inch) and thescreen angle θ for a printed document of which the image fine structuresare to be predicted on a proof (which is not the proof to be producedwith a printing machine but, as will be discussed below in detail, animage which is to be examined and corrected on a continuous-tone printeror a display by a density gradation process). In the embodiment underdiscussion, the screen ruling LPI is set at 175 and the screen angle at75°, 45°, 0° and 15° for the four plates C, M, Y and K, respectively.Needless to say, any desired values can be selected for the screenruling LPI and the screen angle θ.

The moiré intensity input device 15 is also composed of a keyboard, amouse and so forth and provides a means of inputting the intensity ofmoiré in simulation of image fine structures. When simulating moiré,line discontinuities, imperfections in straight lines and other imagefine structures on a proof, the moiré intensity input device 15 suppliesthe image fine structure predicting and processing block 22 with theratio at which the amplitude of image fine structures (which arehereinafter typified by moiré) to be simulated on a proof is adjusted inaccordance with the type of the printing machine to be actually employedin producing a printed document. The ratio of this adjustment, alsoreferred to as “coefficient of emphasis α” of moiré intensity, is reliedupon in the image fine structure predicting and processing block 22 toadjust the weight which is dependent on the period of the grid patternthat is determined by the screen ruling LPI and the screen angle θ for aprinted document. For further details of the coefficient of emphasis α,see below.

The intensity of moiré varies with the specifications of the printingmachine to be used even if the original image and the printingconditions (screen ruling, screen angle and print density) are the same.The difference or the variation defines general prediction since itoriginates not only from mechanical factors such as the printingpressure applied by the printing machine but also from other factorsassociated with software (e.g. halftoning algorithms) and/or hardware.

In order to accommodate this difference, the method of the inventionprovides a means of adjusting the intensity of moiré on the simulatedimage in accordance with the intensity of moiré which is variable withthe printing machine to be used in producing a printed document.Briefly, depending upon whether the printing machine is prone to producemoiré, the means of inputting the intensity of moiré during simulationis used to adjust the intensity of moiré on the proof.

The sharpness intensity input device 16 is also composed of a keyboard,a mouse and so forth and provides a means of inputting the intensity ofsharpness during simulation of image fine structures. When simulatingmoiré, line discontinuities, imperfections in straight lines and otherimage fine structures on a proof, the sharpness intensity input device18 supplies the image fine structure predicting and processing block 22with the ratio β at which the sharpness of the image being simulated ona proof is emphasized in accordance with the type of the printingmachine to be actually employed in producing a printed document. Theratio of sharpness emphasis β is relied upon in the image fine structurepredicting and processing block 22 to adjust the intensity of asharpness filter which is to be applied to the weight which is dependenton the period of the grid pattern that is determined by the screenruling LPI and the screen angle for a printed document. For furtherdetails of the ratio β, see below.

The intensity of sharpness varies with the specifications of theprinting machine to be used even if the original image and the printingconditions (screen ruling, screen angle and print density) are the same.The difference or the variation defines general prediction since itoriginates not only from mechanical factors such as the printingpressure applied by the printing machine but also from other factorsassociated with software (e.g. halftoning algorithms) and/or hardware.

In order to accommodate this difference, the method of the inventionprovides a means of adjusting the intensity of sharpness of thesimulated image in accordance with the intensity of sharpness which isvariable with the printing machine to be used in producing a printeddocument. Briefly, depending upon the ease with which the printingmachine produces sharpness, the means of inputting the intensity ofsharpness during simulation is used to adjust the intensity of sharpnessof the proof.

As will be described more specifically on the pages that follow, theprocess to be performed by the image fine structure predicting andprocessing block 22 is basically such that the pixel values of theindividual images that compose the original image data I are convertedto those which have been subjected to weighted averaging by weightswhich are dependent on the period of the grid pattern as determined bythe screen ruling LPI and the screen angle θ for a printed document. Inthe method of the invention, in order to ensure that the intensity ofmoiré (and other image fine structures that appear in a simulated image)is freely adjusted in accordance with the type of the printing machineto be used to produce the printed document, weights which are dependenton the period of the grid pattern is adjusted in accordance with thecoefficient of emphasis α of moiré intensity and, preferably, sharpeningis applied to the adjusted weights, with the intensity of the sharpeningprocess being optionally adjusted by the ratio of sharpness emphasis βin accordance with the type of the printing machine to be used toproduce the printed document. The eventually obtained image data whichare composed of weighted averaged pixel values are supplied into a printcolor predicting and processing block 23 and/or a display colorpredicting and processing block 24 as image data I′ which are emphasizedin image fine structure and which have the moiré intensity and,optionally, the sharpness intensity adjusted in accordance with the typeof the printing machine to be used to produce the printed document (I′being the image data for the CMYK plates and consisting of image data C′for plate C, image data M′ for plate M, image data Y′ for plate Y andimage data K′ for plate K).

The print color predicting and processing block 23 and the display colorpredicting and processing block 24 are not the essential part of theinvention and need not be described in detail. It suffices here to saythat in the block 23, the image fine structure emphasizing image data I′for CMYK are first converted to device independent data which areindependent of devices (i.e., printing device, CRT, photographic deviceand LED), for example, tristimulus value data XYZ, which in turn areconverted to printer's red-green-blue image data RGB (device dependentdata) based on the colorimetric values of sample colors and thensupplied into the printer 17. Similarly, in the block 24, the image finestructure emphasizing image data I′ for CMYK are first converted todevice independent image data such as tristimulus value data XYZ, whichin turn are converted to display's image data RGB which take intoaccount color temperatures, viewing light source and other conditionsand then supplied into the display 18.

The printer 17 in the image output device 19 is not limited to anyparticular type of printers as long as it is a continuous-tone colorprinter capable of producing a hard proof HP which has acontinuous-gradation image formed on a sheet. A preferred example is aso-called hard proof (HP) producing device which forms an image on adonor film by a density gradation process in which the intensity andtime of emission of three primary colors from an LED (light-emittingdiode) or a laser are digitally controlled pixel for pixel and the imageis transferred to an image-receiving sheet, whereby image formation iseffected on the sheet. The continuous-tone printer 17 having thisfunctional feature is considerably less expensive than ordinary colorprinting machines which effect printing using PS plates prepared frompress plates. The printer 17 typically has a resolution of 200-400 dpi,which is considerably lower than the printing resolution, say, 2000 dpibut it is this low resolution that enables the representation of moiréand other image fine structures that will appear in printed documents.In order to ensure that moiré and other image fine structures can befreely represented on a hard proof HP, their intensity adjusted freelyand the intensity of the sharpness of the formed image also adjustedfreely, the printer 17 preferably has a resolution of about 400 dpi.

The display 18 which is the other component of the image output device19 may be a color CRT monitor, a color LCD (liquid-crystal display)monitor, a color plasma display or the like. While a display allowingfor adjustment of color temperature, white balance, gamma characteristicand so forth is preferred, any devices that represent a so-called softproof SP may be employed without particular limitation. If the purposeis only for checking image fine structures, ordinary inexpensive colorCRT monitors and the like may be employed although they are not capableof adjustment of the various characteristics mentioned above.

We next describe the operating theory of the image fine structurepredicting and processing block 22. In order to ensure that the pixelvalues of the original image data I which have been separated into thepixels of the CMY plates or CMYK plates are converted to pixel valuesfor predicting moiré and other image fine structures that will appear ina printed image in a manner dependent upon the combinations of screenruling, screen angle and the contents (text, line and pictures) of theoriginal image, the prediction and processing of image fine structuresinvolves determining the weights which are dependent on the period ofthe grid pattern determined by the screen ruling LPI and the screenangle θ for the values of the pixels whose values are to be converted(these pixels are hereinafter sometimes referred to as “pixels undercalculation”) and nearby pixels, adjusting the thus determined weightsby the coefficient of emphasis α of moiré intensity so as to producemoiré (and other image fine structures) having an intensity ofoccurrence that complies with the type of the printing machine to outputa printed document, and effecting weighted averaging of the pixel valueswith the adjusted weights, thereby converting them to the desiredvalues.

Two basic flows of the process of weighted averaging of the pixels undercalculation and nearby pixels in the present invention are shown in FIG.2; the first flow consists of three steps, i.e., determining positiondependent weights in accordance with the screen ruling LPI and screenangle θ which are entered by the screen attribute input device 14 (stepS1), adjusting the position-dependent weights in accordance with thecoefficient of emphasis of moiré intensity α which is entered by themoiré intensity input device 15 (step S3), and determining the values ofthe respective pixels by performing weighted averaging of the values ofthe pixels under calculation and nearby pixels in accordance with theadjusted weights; and the second flow consists of four steps, i.e.,determining position dependent weights in accordance with the screenruling LPI and screen angle θ (step S1), emphasizing the weights (stepS2), adjusting the emphasized weights in accordance with the coefficientof emphasis of moiré intensity a (step S3), and determining the valuesof the respective pixels by performing weighted averaging of the valuesof the pixels under calculation and nearby pixels in accordance with theadjusted weights (step S4).

Determination of position dependent weights in step S1 may beaccomplished by generating them using a certain function but this is atime-consuming approach and may be replaced by a method based onreference to a lookup table.

Step 3 is a characterizing part of the invention and in order to adjustthe position dependent weights determined in step S1, both thedetermined weights and a function which takes the value “1” at theposition of any pixel whose value is to be converted and which takes thevalue “0” in other positions are interpolated or extrapolated using thecoefficient of emphasis of moiré intensity α. Needless to say, othermethods may be adopted to adjust the ratio of moiré emphasis.

The weighted averaging to be performed in step S4 may be accelerated bya so-called “stockastic multiplication/addition process” which employs aconvolution integrated weighting matrix (hereinafter also referred to asa “weighting filter”) having a certain mask size (the process mayhereinafter be referred to as “a filtering process”). When weightedaveraging is to be performed by filtering with a weighting matrix, thematrix is preferably adjusted for the weights.

When designing a weighting matrix, anti-aliasing as indicated by step S5in FIG. 2 may be performed using a decreasing filter (also referred toas “attenuating filter”) in which the position dependent weight forpixels neighboring the pixel under calculation decreases with theincreasing distance from the pixel under calculation in order to providea smooth (natural) continuity for the image (picture) formed of thefiltered pixels.

In order to ensure that the image on a proof HP or SP has the samedegree of sharpness as the image to be formed on a printed document,sharpening may be effected as indicated by step S6 in FIG. 2 such that asharpness filter (hereinafter referred to as “emphasizing filter”) isallowed to act on the weighting filter processed by the decreasingfilter and the resulting weighting matrix filter is used as a matrixfilter in the weighted averaging step. Dots to be formed on a sheet bymeans of the printer 17 are usually such that the recording dotsproduced by means of a laser beam or the like assume a Gaussian or likeshape and thereby produce an image with blurred edges; to deal with thisproblem, the sharpening process of step S6 is effective.

Step 7 is another characterizing portion of the invention and comprisesadjustment of the intensity of sharpness. Briefly, in order to adjustthe ratio of emphasis by the sharpness filter to be applied to theweighting filter after processing by the decreasing filter, both thesharpness filter and a filter which takes the value “1” at its centerposition and which takes the value “0” in the other positions aredivided either internally or externally using the ratio of sharpnessemphasis β. Needless to say, other methods may be adopted to adjust theratio of sharpness emphasis.

In the example shown in FIG. 2, step S3 of weight adjustment is followedby step S4, in which weighted averaging is performed in accordance withthe adjusted weights, thereby determining weighted-averaged pixelvalues. This is not the sole case of the invention and in an alternativecase, the position dependent weights determined in step S1 or S2 aredirectly used in step S4 to determine weighted-averaged pixel valueswhich, together with the pixel values of the original image, areinterpolated or extrapolated using the coefficient of emphasis of moiréintensity α, whereby the average pixel values weighted by the adjustedweights are determined to eventually create a result which is equivalentto the weight adjustment to be performed in step S3.

The operating theory of the image fine structure predicting andprocessing block 22 is described below in greater details.

As already mentioned, weighted averaging is performed on the originalimage data for the CMYK plates which compose the original image data I.Take, for example, C plate. If the pixel data (pixel value) atcoordinates (i,j) on the output resolution (proof resolution) arewritten as C(i,j) and the pixel data (pixel value) at coordinates (i,j)resulting from the weighted averaging of the pixels under calculationand nearby pixels are written as Ca(i,j), the pixel values Ca(i,j) afterprocessing (conversion) can be expressed by the following equation (8):$\begin{matrix}{{{Ca}\left( {i,j} \right)} = {\sum\limits_{k = {- m}}^{m}\quad {\sum\limits_{l = {- n}}^{n}\quad \left\{ {{Weight}\quad \left( {i,j,k,l,{LPIc},{\theta \quad c}} \right) \times {C\left( {{i + k},{j + l}} \right)}} \right\}}}} & (8)\end{matrix}$

The calculation for weighted averaging (Σ_(k=−m) ^(m)Σ_(l=−n) ^(n)) ofnearby pixels is performed on pixels C(i+k, j+1) in a rectangular rangewhich surrounds the original image C(i,j) by a size of (2m+1)×(2n+1).Stated more specifically, k ranges from −m to m and l from −n to n inC(i+k, j+l), where 2m+1 and 2n+1 are natural numbers and represent thewidth and height (which may collectively be referred to as “size”) ofnearby pixels centered at the position (i,j) which represent thecoordinates of the pixel under calculation.

The equation (8) is intended for weighted averaging, so it is normalizedsuch that the total sum of the individual values of the weight (alsocalled “weight function”) Weight(i,j,k,l,LPIc,θc) is at unity (1.0); theprocess of normalization is hereinafter sometimes referred to as“standardization”. Although equation (8) concerns C plate, similarequations can be written for the other plates M, Y and K by replacingthe pixel value C(i,j) of the original image with M(i,j), Y(i,j) andK(i,j), respectively, and by replacing the pixel value Ca(i,j) of theprocessed image with Ma(i,j), Ya(i,j) and Ka(i,j), respectively. Thecoordinates (i,j) are on a Cartesian two-dimensional xy image space. Thescreen ruling for C plate LPIc may be replaced by LPIm, LPIy and LPIkfor plates M, Y and K, respectively, and the screen angle θc for C platemay be replaced by θm, θy and θk for plates M, Y and K, respectively.

In equation (8), the weight function Weight(i,j,k,l,LPIc,θc) is intendedfor causing so-called “frequency interference” by enhancing the weightof the value of a pixel which is among the pixels that compose theoriginal image and which is located at a position corresponding to aspecified point on a grid having a certain periodic pitch (which isdependent on the screen ruling LPIc) and a certain inclination (which isequivalent to the screen angle θc).

In the case under discussion of the present invention, in order toensure that the intensity of moiré (and other image fine structures)being simulated is adjusted in accordance with the type of the printingmachine to be used to produce a printed document, the weight functionWeight(i,j,k,l,LPIc,θc) to be used in step S4 (weighted averaging) isone that is newly adjusted in intensity in step S3 by the coefficient ofemphasis of moiré intensity α which has been entered by moiré intensityinput device 15. However, the position dependent weight functiondetermined in step S1 and that determined in step S2 are both inherentlya weight function, so needless to say, these weight functions maydirectly be used in step S4 to achieve equally good results in theprediction of moiré and other image fine structures.

We now describe the method by which the intensity of moiré (and otherimage fine structures) being simulated is adjusted in accordance withone characterizing feature of the invention.

In the case of printing, if the screen ruling LPI and the screen angle θare determined, the LPIc and θc for the C plate may be regarded asconstants and, hence, the weight function Weight(i,j,k,l,LPIc,θc) can bewritten as Weight(i,j,k,l) for each of the CMYK plates. Therefore, inthe following description, the weight function with an adjusted moiréintensity as determined in step S3 is represented by a moiré intensityadjusted filter F′(i,j,k,l) for each of the CMYK plates and the positiondependent weight function as determined in step S1 or S2 is representedby a position dependent filter F(i,j,k,l) for each of the CMYK plates.

In the case under consideration of the present invention, the newlyintensity adjusted filter F′(i,j,k,l) can be expressed by the followingequation (1):

F′(i,j,k,l)=α{−E(k,l)+F(i,j,k,l)}+E(k,l)  (1)

where E(k,l) is a coefficient matrix which has a coefficient of 1.0 atthe center and a coefficient of 0.0 in the other positions;

E is not dependent on the position of the coordinates (i,j) of a givenpixel.

In equation (1), a is the coefficient of emphasis of moiré intensity; ifα=1, the moiré intensity is just the same as what it was before emphasisand is equal to F(i,j,k,l) and if α=0, the moiré intensity is zero,which means no moiré simulation is being effected and the original imageis just what you get.

In equation (1), i and j represent the coordinates of the position of agiven pixel in the image; if the size (in pixels) of a rectangular imageis designated by width x height, i ranges from 0 to (height −1) and jranges from 0 to (width −1).

In equation (1), k and l are auxiliary variables for the convolutionintegral.

Using the thus determined moiré intensity adjusted filter F′(i,j,k,l),convolution integral is performed in step S4. If the intensity adjustedfilter F′(i,j,k,l) is regarded as a position dependent (2m+1)×(2m+1)filter, convolution integral is carried out in accordance with thefollowing equation (9): $\begin{matrix}{{D^{\prime}\left( {i,j} \right)} = {\sum\limits_{k = {- m}}^{m}\quad {\sum\limits_{l = {- m}}^{m}\left\{ {{F^{\prime}\left( {i,j,k,l} \right)} \times {D\left( {{i + k},{j + l}} \right)}} \right\}}}} & (9)\end{matrix}$

where D(i,j) is a pixel value for either one of the CMYK plates preparedfrom the original images and D′(i,j) is a pixel value for the same plateafter processing; −m≦k≦m and −m≦l≦m.

In the present irivention, the pixel value D′(i,j) is determined byweighted averaging with the intensity adjusted filter F′(i,j,k,l).Alternatively, D′(i,j) may be determined in the following way. If thepixel value determined by weighted averaging with the position dependentfilter F(i,j,k,l) is written as Da(i,j), it is given by the followingequation (10): $\begin{matrix}{{{Da}\left( {i,j} \right)} = {\sum\limits_{k = {- m}}^{m}\quad {\sum\limits_{l = {- m}}^{m}\left\{ {{F\left( {i,j,k,l} \right)} \times {D\left( {{i + k},{j + l}} \right)}} \right\}}}} & (10)\end{matrix}$

By substituting equation (1) into equation (9) and also substitutingequation (10) into the resulting equation, the converted pixel valueD′(i,j) is given by the following equation (11):

D′(i,j)=αDa(i,j)+(1−α)D(i,j)  (11)

This is how weighted averaging with moiré intensity adjusted weights isperformed in the image fine structure predicting and processing block22.

The operating theory of the weighted averaging to be performed by theimage fine structure predicting and processing block 22 is nowillustrated visually by means of diagrams. The operating theory of theweighted averaging process to be described below will hold irrespectiveof whether moiré intensity adjustment is performed or not. Suppose herethat FIG. 3A represents conceptually the original image I as separatedinto pixels of 400 dpi. Also suppose that FIG. 3B represents acoordinate system (hereinafter also referred to as “screen gridcoordinate system”) PC having a screen ruling of 175 and a screen angleof 45 degrees in accordance with the printed document for which a proofis to be prepared. When the grid shown in FIG. 3B with respect to thecoordinate system PC having the screen ruling of 175 and the screenangle of 45 degrees is placed on the concept of the original image Ishown in FIG. 3A, a superposed image SI will result as shown in FIG. 3C.

Those areas of the superposed image SI shown in FIG. 3C which arepainted in black correspond to the pixels bridging the grind points inthe coordinate system PC having the screen ruling of 175 and the screenangle of 45 degrees. By increasing the weights of the values of thesepixels painted in black (in fact, in order to achieve weightedaveraging, the weights of the values of the surrounding pixels shown asclear spots are reduced simultaneously), the weights of the values ofpixels located in positions corresponding to specified points on theabove-described grid can be increased.

For better understanding by intuition, let assume the case of extremeweighting such as where only two values of 1 and 0 are taken. This casemay be interpreted as being based on simple mathematical operations forimage reduction by leaving only the black pixels intact while discardingthe clear spots. In other words, it may be held that moiré can begenerated by the same principle as that for moiré generation by samplingwhich occurs when mathematical operations for image reduction areperformed on an image of interest. In FIG. 3B, the reciprocal of thescreen ruling, or {fraction (1/175)} (in inches), may be referred to asthe pitch of the screen. Therefore, each of the square units in thescreen has a size of {fraction (1/175)}×{fraction (1/175)} (in²).

Returning to FIG. 2, the position dependent weights for weighting arecalculated (determined) in step S1 and there are two basic methods forachieving this. In the first method, a threshold matrix for each of theCMYK plates for use in the halftoning step in the printing process(being also called “a threshold template” or “a rational number screentemplate”, the matrix is commonly named “a supercell”) is directly used(i.e., as a lookup table) for weight calculation.

FIG. 4A shows a portion (e.g. 9 pixels) of either one of the CMYK platesrepresenting the original image I which is shown conceptually in FIG.3A. The values of the respective pixels are represented by a, b, . . .i. The coordinate system is expressed generally by (i,j) and the pixelvalues a, b, . . . i [in which i is different from i in the coordinatesystem (i,j)] may typically take any one of the values 0-255 whichrepresent an 8-bit gradation.

Suppose that FIG. 4B shows a portion of a halftoning threshold matrixTM′ which is to be used in producing a printed document of 2000 dpi. Onepixel of 400 dpi corresponds to 5×5=25 pixels of 2000 dpi (or 25elements of a threshold matrix TM′). Also suppose that the partialthreshold matrices each consisting of 5×5 elements have threshold valuesof A, B, . . . I for the central element as shown in FIG. 4B. Each ofthe threshold values A, B, . . . I may typically be one of the values of0-255 which compose an 8-bit gradation. It should also be noted that ifthe threshold matrix TM has a size equivalent to 215×215 elements(representing 46225 threshold values), it corresponds to a size of 43×43pixels in the original image I. The coordinate system on the space of2000 dpi may be expressed as (i′,j′).

Referring back to FIG. 4A, suppose that the value of the pixel at thecenter of the original image I which is designated by e is subjected toweighted averaging using a position dependent weighting matrix which isyet to be adjusted in the ratio of emphasis of moiré intensity. Theprocessed pixel value ea may be expressed by the following equation(12):

e ^(a)=(Aa+Bb+. . . +Ii)/(A+B+. . . I)  (12)

In this case, the respective weights are represented by A/(A+B+. . . I),B/(A+B+. . . I), . . . I/(A+B+. . . I); hence, the total sum of theweights is at unity. Thus, the weighted averaging process underconsideration is such that a matrix of the original image consisting of3 pixels wide by 3 pixels high surrounding the pixel e whose value is tobe converted (see FIG. 4A) is filtered by convolution integration with a3×3 weighting matrix in which the respective elements have weights ofA/(A+B+. . . I), B/(A+B+. . . I), . . . I/(A+B+. . . I). This filteringprocess is also known as a “stochastic multiplication/addition process”.

If a position dependent weighting matrix after adjustment with thecoefficient of emphasis of moiré intensity is used in weightedaveraging, the processed central pixel value e′ may be expressed by thefollowing equation (13): $\begin{matrix}\begin{matrix}{e^{\prime} = \quad {\left\{ {{\alpha \left( {{Aa} + \cdots + {Dd} + {Ff} + \cdots + {Ii}} \right)}/\left( {A + B + \cdots + I} \right)} \right\} +}} \\{\quad {\left\{ {{\alpha \quad {E/\left( {A + B + \cdots + I} \right)}} + \left( {1 - \alpha} \right)} \right\} e}} \\{= \quad {{\alpha \quad e_{a}} + {\left( {1 - \alpha} \right)e}}}\end{matrix} & (13)\end{matrix}$

Obviously, the pixel value e′ can also be determined by α adjustment ofthe pixel value ea previously obtained by weighted averaging. Inequation (13), the respective weights are represented by αA(A+B+. . .+I), αB(A+B+. . . +I), αE(A+B+. . . +I) +(1−α), . . . , αI(A+B+. . . +I)and, in this case, the total sum of the weights is also at unity.

When calculating the weight by direct use of the threshold matrix TM′for each of the CMYK plates used for the halftoning step in the printingprocess, the weight may originate from the threshold value of theelement located at any specified position in each of the partialthreshold matrices of 5×5 elements which compose the pixel-relatedthreshold matrix TM′, for example, at the center of each partialthreshold matrix or at the top left corner of it. Alternatively, theaverage value for the partial threshold matrices of 5×5 elements may beused as the weight. The thus determined weight may subsequently beadjusted by the coefficient of emphasis of moiré intensity α.

Described above is the first method of providing an adjusted weightswhich is based on weight calculation by direct use of the thresholdmatrix TM′ for each of the CMYK plates used for the halftoning step inthe printing process.

The second method of providing adjusted weights involves the use of afunction which is characterized by monotonic weight decrease or increasefrom the center outward. FIG. 5A shows a screen grid represented by thesame coordinate system PC as shown in FIG. 3B and the center from whichthe weight decreases outward may be each grid point 31 (where two linesintersect at right angles), or the midpoint 32 between two adjacent gridpoints or any single point within each grid unit. Consider, for example,a function in which the midpoint 32 between two adjacent grid points isthe center from which the weight decreases monotonically. In terms of auv coordinate system of the type shown in FIG. 5A, a weight functionexpressed by the following equation (14) can be established for eachgrid unit (the length of each side of one grid unit is equal to thereciprocal of the screen ruling LPI):

f(u,v)=0.5−{(u−0.5)²+(v−0.5)² }/Σf(u,v)  (14)

A schematic waveform of the weight function f(u,v) may be placed on thecoordinate system PC having a screen ruling of 175 and a screen angle of45 degrees to produce a superposed image SI′, which is shown inperspective in FIG. 5B.

In practice, the weight for a specified pixel position (i,j) in theoriginal image I shown in FIG. 3A may be calculated by the followingprocedure. Write (u,v) for the position on the grid space coordinatesystem PC which corresponds to the pixel position (i,j) and also writeR, LPI and θ for the resolution (dpi) of the original image I, screenruling and screen angle, respectively. Then, the position (u,v) can bedetermined by the following equation (15):

u={cos(−θ)×i+sin(−θ)×j}×L/R

v={sin(−θ)×i+cos(−θ)×j}×L/R  (15)

Substituting the determined position (u,v) into equation (14), one canobtain the weight for the position (u,v), which may safely besubstituted for the weight at the specified pixel position (i,j). Thethus determined weight may subsequently be adjusted by the coefficientof emphasis of moiré intensity α.

Described above is the second method of determining adjusted weightsafter weight calculation. The weighting weights, whether they arecalculated by the first or second method, may be employed in predicting(or simulating) image fine structures to ensure equally satisfactoryresults.

The method of determining adjusted weights after weight calculation isdescribed below in much greater details with reference to the flow chartshown in FIG. 6. Considering the convenience in calculations and theshortness of calculation times, the following description assumes thatweights are calculated by direct use of the threshold matrix TM′ foreach of the CMYK plates for use in the halftoning step in the printingprocess.

Given this assumption, let us first describe an exemplary process ofpreparing a weighting matrix for the C plate on the basis of specificnumerical values. Weighting matrixes for the MYK plates can be preparedby procedures which are entirely the same as described below. Thefollowing description also assumes that the printer 17 is acontinuous-tone color printer which, as already mentioned hereinabove,has an output resolution of 400 dpi and which is capable of operating bya so-called “density gradation process” to provide 256 densitygradations for each of the RGB colors. It is also assumed that theresolution of the original image I has been adjusted to 400 dpi (equalto the output resolution) by the preliminary resolution conversion withthe block 21.

The screen ruling LPI and the screen angle θ which are used insimulating image fine structures on a hard proof HP being output fromthe printer 17, namely, for predicting the image fine structures whichwill appear in a printed document, may take any desired values but, inthe following description, LPI is assumed to be 175 whereas θ is assumedto take the values of 75, 45, 0 and 15 in degrees for the plates C, M, Yand K, respectively.

In the printing process, the screen angle is dealt with as varying from0 to 180 degrees because the screen has no geometric symmetry withrespect to 90° rotation. However, if attention is paid only to thearrangement of dots, the screen space is a Cartesian coordinate systemin which the two axes intersect at right angles, so the range through a0-90° arc will suffice for the purpose of predicting the image finestructures which will appear in a printed document. With a screen inwhich 0=90-180°, values obtained by conversion in accordance with thefollowing equation (16) may be employed:

θ=0%(90 deg.)  (16)

where “%” represents a residue calculation in which θ on the right sideis divided by 90 degrees to leave θ on the left side as the remainder.

As will be understood from FIG. 3C, the mask size of a weighting matrix(hereinafter also referred to as “a weighting filter”) would besufficient to represent image fine structures if it is determined to besuch that at least one pixel corresponding to the output resolution ispresent within a single grid unit (screen cell). In the case underconsideration, a weighting matrix will be prepared which has a 3×3 masksize as specified by the following equation (17) which means that theoutput resolution is divided by the screen ruling and the fractionalpart of the quotient is raised to a whole number:

INT(output resolution/screen ruling)+1 INT(400/175)+1=3  (17)

In equation (17), INT(X) signifies a mathematical operation for leavingonly the integral part of X intact.

Given a mask size of 3×3 for the weighting matrix and if the screenruling LPI and the screen angle θ in equation (8) are determined, thesetwo parameters can be regarded as constants; therefore, equation (8) canbe rewritten as follows: $\begin{matrix}{{{Ca}\left( {i,j} \right)} = {\sum\limits_{k = {- 1}}^{1}\quad {\sum\limits_{l = {- 1}}^{1}\left\{ {{Weight}\quad \left( {i,j,k,l} \right) \times {C\left( {{i + k},{j + l}} \right)}} \right\}}}} & (18)\end{matrix}$

For the sake of convenience in the following description, rewriteequation (18) as follows: $\begin{matrix}{{{Ca}\left( {i,j} \right)} = \quad {\underset{k = {- 1}}{\sum\limits^{1}}{\overset{1}{\sum\limits_{l = {- 1}}}\left\{ {{Weight}\quad \left( {i,j,k,l} \right) \times {C\left( {{i + k},{j + l}} \right)}} \right\}}}} \\{= \quad {\sum\limits_{k}{\sum\limits_{l}\left\{ {{Weight}\quad \left( {i,j,k,l} \right) \times {C\left( {{i + k},{j + l}} \right)}} \right\}}}}\end{matrix}$

The calculation of Σ_(k=−1) ^(1Σ) _(l=−1) ¹ is performed on the pixelunder calculation and the nearby pixels within a rectangular range whichsurrounds it by a size of 3×3. Thus, k=−1, 0, 1 and l=−1, 0, 1. In thefollowing description, the calculation of Σ_(k=−1) ¹Σ_(l=−1) ¹ will bedesignated by Σ_(k)Σ_(l). The term weight(i,j,k,l) may represent aweight which is either before or after adjustment of the moiréintensity.

On the pages that follow, the first method of providing adjusted weightswhich is based on weight calculation by direct use of the thresholdmatrix TM′ for plate C will be described in an even more specific waywith reference to FIG. 6.

When determining the values of the respective elements of a 3×3weighting matrix, a rational number screen (also called “rationalscreen”) template may be employed and, as already noted hereinabove, thetemplate is a threshold matrix for use in the printing process (stepS11). If a rational number screen template of 2000 dpi consists of215×215 elements, the size of the template may be defined as beingMMsize×MMsize (MMsize=215).

FIG. 7 shows an example of rational number screen template TM0′ whichhas a resolution of 2000 dpi and a size of MMsize (=215)×MMsize (=215).The coordinate system on the resolution of 2000 dpi may be written as(i′,j′); therefore, on the rational number screen template TM0′ of 2000dpi, the coordinate i′ takes one of the values ranging from 0 to 214 andso does the coordinate j′. The threshold value th0′ is assigned 215×215elements and if it is 8-bit data, th0′ takes one of the values in therange from 0 to 255.

In preparation for the case where a 3×3 weighting matrix of 400 dpiwhich is allowed to act on the original image data I is constructed fromthe rational number screen template TM0′ of 2000 dpi (step S12), onemust consider that 5×5 dots (elements) of 2000 dpi correspond to asingle dot of 400 dpi and he may then construct a threshold templateconsisting of 43×43 threshold values extracted from the centers(delineated by thick lines) of partial rational number screen templateseach composed of 5×5 elements as shown in FIG. 4B (step S12).

FIG. 8 shows an example of the thus constructed threshold template TM0which consists of 43×43 threshold values th0(i,j). In this figure, thethreshold template TM0 is shown to have a mask size of Msize and in thecase under consideration, the mask size Msize is equal to 43.

In the next step, a 3×3 weighting matrix wgt0(i,j,k,l) is constructedfrom the threshold template (step S13) and this matrix may be expressedby the following equation (19).

wgt0(i,j,k,l)=th0(i+k, j+l)/ss(i,j)  (19)

where k, l=−1, 0, 1 and ss(i,j) means Σ_(k)Σ_(l){th0(i,j,k,l)} (where kand l each take a value in the range from −1 to 1)

Substituting the resulting 3×3 weighting matrix wgt0(i,j,k,l) asF(i,j,k,l) into equation (1), one can adjust said matrix with thecoefficient of emphasis of moiré intensity α, whereby an adjustedweighting matrix wgt0′(i,j,k,l) can be determined in accordance with thefollowing equation (20) (step S27):

wgt0′(i,j,k,l)=α{−E(k,l) +wgt0(i,j,k,l)}+E(k,l)  (20)

Using the thus obtained 3×3 weighting matrix, mathematical operationsfor weighted averaging are performed by the specific sequence shown inFIGS. 9A-9E. Since wgt0(i,j,k,l) is wgt0(i,j,k,l)=th0(i+k, j+l)/ss(l,l),the first step is such that th0(l,l)=54 and the nearby threshold valuesin a 3×3 matrix having th0(l,l) in the center are extracted from thethreshold template TM0 shown in FIG. 8 (see FIG. 9A). In the next step,Σ_(k)Σ_(l)ss (l,l) is determined by summing up the threshold valuesincluding th0(l,l) and the nearby threshold values in the 3×3 matrix andthe result is

Σ_(k)Σ_(l) ss(l,l)=208+148+. . . +77=1268.

Hence, the 3×3 weighting matrices wgt0 are obtained by dividing thethreshold values of the elements of interest by Σ_(k)Σ_(l)ss(l,l)=1268.The value of Σ_(k)Σ_(l)wgt0(l,l,k,l) which is the total sum of theindividual 3×3 weighted matrices wgt0 is always equal to unity (see FIG.9C).

If the coefficient of emphasis of moiré intensity α is 0.9, theindividual elements of the 3×3 weighting matrix wgt0 will take thevalues shown in FIG. 9D.

If the pixel value e in the original image data I of 400 dpi which isshown schematically in FIG. 4A to consist of 3×3 pixels is converted toe′ by weighted averaging with weights adjusted by moiré intensity, thenew pixel value e′ is obtained as e′=(a×0.148+b×0.105+. . . +e×0.138+. .. i×0.055)=0.9×(a×208+b×148+. . . +e×54+. . . +i×77)/1268+e×0.1 (seeFIG. 9E).

Consider here that the pixel value f next to the pixel e is converted tof′ by weighted averaging with adjusted weights. In this case, the newpixel value f′ can be obtained by the following procedure: the pixel atthe ij coordinates of (2,1) in FIG. 8 is taken as the center and thethreshold values including this center value and the nearby thresholdvalues in a 3×3 matrix, namely, 148, 244, 60, 54, 159, 84, 220, 77 and44, are used to first prepare a 3×3 weighting matrix wgt0(2,1) and,then, using the coefficient of emphasis of moiré intensity α which isset at 0.9, a 3×3 adjusted weighting matrix wgt0′(2,1) is constructedfrom the wgt0(2,1) in accordance with the equation (20) and allowed toact on the pixel value f and the nearby pixels in the 3×3-matrix in theoriginal image data I, thereby effecting weighted averaging in the samemanner as described in connection with the pixel value e.

In practice, the 3×3 weighting matrices wgt0 and wgt0′ need be providedin as many units as there are positions of screen cells (grid units)expressed in coordinates (i,j). In the case under discussion, thenecessary number is 43×43 (=Msize×Msize) and the coordinates of screencell position limited by the number Msize×Msize are expressed by(i%Msize, j%Msize), in which i%Msize signifies the remainder that isleft after i is divided by Msize (take, for example, the case of i=1,44, 87; i%Msize is the same for all values of i and it is at unity).Therefore, the image data after weighted averaging, namely, the imagedata Ca(i,j) after emphasis of image fine structures are obtained by thefollowing equation (21) which is transformed from equation (18),provided that the image data Ca(i,j) represent converted data eitherprior to or after adjustment of moiré intensity:

Ca(i,j)=Σ_(k)Σ_(l){Weight(i%Msize,j%Msize,k,l) ×C(i+k, j+l)}  (21)

Thus, the original image data I are subjected to weighted averaging inaccordance with equation (21) to thereby produce image data Ca(i,j)processed by weighted averaging. Of the pixels in the original imagedata I, those which are located on the four sides should not contain theelements of a weighting matrix but it is cumbersome to determine suchelements by mathematical operations. What is more, important picturesare not usually present on the four sides of the original image data I.Therefore, in the embodiment under consideration, the pixels located onthe four sides of the original image data I are not subjected toweighted averaging but are used as such.

The image data Ca(i,j) obtained by weighted averaging in accordance withequation (21) using the adjusted 3×3 weighting matrix wgt0′ as theweight function Weight(i%Msize,j%Msize,k,l) are converted to RGB imagedata by the predicting and processing block 23 or 24 shown in FIG. 1 andthe resulting RBG image data are supplied into the printer 17 forproducing a hard proof HP or into the display 18 for producing a softproof SP. Moiré, line discontinuities and other image fine structuresappear in the images on both proofs with their intensity adjusted inaccordance with the type of the printing machine to be used to produce aprinted document (also see FIG. 6).

We now describe another example of constructing a 3×3 weighting matrix.This example is intended to implement the weight emphasizing step S2already explained above with reference to FIG. 2.

For weight emphasis, a 3×3 weighting matrix wgt1 (to be described lateron) may be constructed after spatial emphasis of the respectivethreshold values th0′ in the threshold template TM0′ of 2000 dpi shownin FIG. 7 or it may be constructed direct from the already preparedthreshold template TM0 of 400 dpi shown in FIG. 8.

In the case of emphasizing the respective threshold values tho′ in thethreshold template TM0 of 2000 dpi shown in FIG. 7, emphasized thresholdvalues th1′ can be obtained by performing a cubic normalizing operationin accordance with the following equation (22) (step S14):

th1′(i′,j′)=th0′(i′,j′)³/255²  (22)

In the case of using the threshold template TM0 of 400 dpi shown in FIG.8, emphasized threshold values th1 can be obtained by performing a cubicnormalizing operation in accordance with the following equation (23)(step S14):

th1(i,j)=th0(i,j)³/255²  (23)

FIG. 10 shows a threshold template TM1′ of 2000 dpi which is composed ofthe emphasized threshold values th1′ obtained as the result ofcalculation by equation (22).

FIG. 11 shows a threshold template TM1 of 400 dpi which is composed ofthe emphasized threshold values th1 obtained as the result ofcalculation by equation (23).

The threshold values th0′ and th1′ which are emphasized values of th0and th1, respectively, are obtained by dividing the cubes of th0 and th1by 255×255 which is a maximum of the threshold values. To give just afew examples, the initial threshold values (th0,th1) of 0, 24, 54, 77,134, 148, 220, 244 and 255 are emphasized to threshold values(th0′,th1′) of 0, 0, 2, 7, 37, 49, 163, 223 and 255, respectively.Obviously, threshold values that are initially close to 255 remainsubstantially the same after emphasis and as they become closer to zero,initial threshold values are emphasized to much smaller values. Notethat every threshold value is either zero or a positive integer (naturalnumber), with zero being a minimum.

The 3×3 weighting matrix wgt1 can be constructed by the followingequation (24) in correspondence with equation (19) using the thresholdvalues th1 in the emphasized threshold template TMI shown in FIG. 11(step S15):

wgt1(i,j,k,l)=th1(i+k, j+l)/ss(i,j)  (24)

where k and l each range from −1 to 1, andss(i,j)=Σ_(k)Σ_(l){th1(i,j,k,l)} (where k and l each range from −1 to1).

Using the thus obtained 3×3 weighting matrix wgt1(i,j,k,l) and adjustingit with the coefficient of emphasis of moiré intensity α in accordancewith equation (1), one can construct an adjusted 3×3 weighting matrixwgt1′(i,j,k,l) in accordance with the following equation (25) whichcorresponds to equation (20) (step S20):

wgt1′(i,j,k,l)={−E(k,l) +wgt1(i,j,k,l)}+E(k,l)  (25)

The image data Ca(i,j) obtained by weighted averaging in accordance withequation (21) using the adjusted 3×3 weighting matrix wgt1′ as theweight function Weight(l%Msize, j%Msize, k,l) are converted to RGB imagedata by the predicting and processing block 23 or 24 as shown in FIG. 6and the resulting RBG image data are supplied into the printer 17 forproducing a hard proof HP or into the display 18 for producing a softproof SP. Moiré, line discontinues and other image fine structuresappear in the images on both proofs with their intensity adjusted inaccordance with the type of the printing machine to be used to produce aprinted document.

Compared to the proofs HP and SP produced using the adjusted 3×3weighting matrix wgt0′, those produced using the adjusted 3×3 weightingmatrix wgt1′ are characterized by more emphasized image fine structures,in other words, they can reproduce moiré, line discontinuities and otherimage fine structures that are sufficiently close to those to beeventually reproduced in a printed document that the latter can bepredicted more accurately.

In fact, however, the adjusted 3×3 weighting matrix wgt1′ has a sideeffect in that the output image on the proofs HP and SP produced usingsaid matrix suffers from slight discontinuities, although very slight,that occur between adjacent pixels at a resolution of 400 dpi, in otherwords, the edges of the image are slightly noticable. Frequencycomponents from pixel periods higher than 400 dpi are also slightlynoticable and this is another side effect of the matrix wgt1′.

In order to eliminate these side effects, one may construct a 3×3weighting matrix wgt2 (details of which will be given later in thisspecification) by allowing a 3×3 low-pass filter to act on the yet to beadjusted 3×3 weighting matrix wgt1. The 3×3 low-pass filter ischaracterized by weight decrease (attenuation) from the center outwardand so named because the center is the component to be reduced. The 3×3low-pass filter (also called “attenuating filter”) may be regarded as ablurring or anti-aliasing filter. In the embodiment under consideration,a Gaussian filter is used as the 3×3 low-pass filter.

FIG. 12 shows the composition of the 3×3 Gaussian filter on the 400 dpispace which is used in the embodiment under consideration. The Gaussianfilter is mathematically expressed by the following equation (26):

Gauss1(k,l)  (26)

where k and l each range from −1 to 1. The 3×3 weighting matrix wgt2 isconstructed by multiplying the 3×3 weighting matrix wgt1 by the Gaussianfilter Gauss1(k,l) and can be expressed by the following equation (27)(step S16):

wgt2(i,j,k,l)

=wgt1(i,j,k,l)×Gauss1(k,l)

=th1(i+k, j+l)

×Gauss1(k,l)/ss(i,j)  (27)

where k and l each range from −1 to 1, ss(i,j)=Σ_(k)Σ_(l){th1(i+k,j+l)×Gauss1(k,l)}, and i and j each range from 0 to Msize−1.

Using the thus obtained 3×3 weighting matrix wgt2(i,j,k,l) and adjustingit with the coefficient of emphasis of moiré intensity α in accordancewith the equation (1), one can construct an adjusted weighting matrixwgt2′(i,j,k,l) in accordance with the following equation (23) whichcorresponds to equation (20) (step S20):

wgt2′(i,j,k,l)=α{−E(k,l) +wgt2(i,j,k,l)}+E(k,l)  (28)

The image data Ca(i,j) obtained by weighted averaging in accordance withthe equation (21) using the adjusted 3×3 weighting matrix wgt2′ areconverted to RGB image data by the predicting and processing block 23 or24 and the resulting RBG image data are supplied into the printer 17 forproducing a hard proof HP or into the display 18 for producing a softproof SP. This provides an output image in which adjacent pixels at theresolution of 400 dpi connect smoothly, which has no frequencycomponents from periods higher than 400 dpi and in which moiré, linediscontinuities and other image fine structures that will appear in aprinted document are faithfully simulated with their intensity adjustedin accordance with the type of the printing machine to be used toproduce the printed document.

We next describe the construction of an adjusted 3×3 weighting matrixwgt5′ which is capable of connecting adjacent pixels even more smoothlythan the matrix wgt2′.

To begin with, the threshold template TM1′ of 2000 dpi level which isshown in FIG. 10 and which is constructed by so-called “cubicnormalization” of threshold values is acted upon by a 15×15 Gaussianfilter Gauss2(m,n) corresponding to the resolution of 2000 dpi (thisadjustment is generally called “resolution conversion”) so as to preparea weight matrix wgt3 of 400 dpi.

FIG. 13 shows an example of the 15×15 Gaussian filter Gauss2(m,n) whichis composed of pixels having the resolution of 2000 dpi. The coordinates(m,n) correspond to the coordinates (i′,j′) on the 2000 dpi space. Asone can see from FIG. 13, the Gaussian filter Gauss2(m,n) has itselements arranged in a fashion that is approximated by Gaussiancharacteristics in which the center value is the greatest and gradualattenuation in value occurs as the pixels depart from the centeroutward.

The Gaussian filter Gauss2(m,n) is allowed to act on the emphasizedthreshold template TM1′ to construct the weight matrix wgt3 of 400 dpilevel in accordance with the following equation (29) (step S22):

wgt3(i,j,m,n)=th1′(5×i+m, 5×j+n) ×Gauss2(m,n)  (29)

where m and n each range from −7 to 7, (i,j) are coordinates on the 400dpi space, where i and j each range from 0 to Msize−1, and (5×i+m,5×j+n) and (m,n) are each coordinates (i′,j′) on the 2000 dpi space,where i′ and j′ each range from 0 to Msize−1. The multiplier 5 of i andj results from 2000 dpi/400 dpi and is a constant for resolutionconversion.

Consider first the case where (i,j) is (0,0) in equation (29). Sincewgt3(0,0,m,n) =th1′(m,n) ×Gauss2(m,n), select the 15×15 matrix at thetop left corner of the threshold template TM1′ shown in FIG. 10 whichhas threshold values of 255, 50, 49 and 12 at the four corners andmultiply the respective elements of the matrix by the respectiveelements of the Gaussian filter Gauss2(m,n) shown in FIG. 13, to therebyconstruct a 15×15 weight matrix wgt3 (0, 0, m, n).

Consider next the case where (i,j)=(1,0) in equation (29). Sincewgt3(1,0,m,n)=th1′(5+m, n)×Gauss2(m,n), select a 15×15 matrix in thethreshold template TM1′ shown in FIG. 10 which is shifted to the rightby i′=5 from the above-mentioned 15×15 matrix at the top left corner ofthe template TM1′ and which has threshold values of 23, 0, 123 and 105at the four corners and multiply the respective elements of the matrixby the respective elements of the Gaussian filter Gauss2(m,n) shown inFIG. 13, to thereby construct a 15×15 weight matrix wgt3(1,0,m,n).Similar procedures are applied to construct weight matrices wgt3 insuccession until wgt3(Msize−1, Msize−1, m,n) is obtained for (i,j)=(Msize−1, Msize−1).

Thus, 43×43 (=Msize×Msize) weight matrices wgt3(i,j) are obtained afterprocessing with the Gaussian filter Gauss2 (m, n). In order to convertthese weight matrices to the same number (43×43) of 3×3 weightingmatrices, one may effect conversion such that a single weight matrixwgt3(i,j) consisting of 15×15 elements corresponds to a single weightingmatrix wgt4(i,j,k,l) (k and l each range from −1 to 1) which consists of3×3 elements.

The respective elements of the stated number (43×43) of 3×3 weightingmatrices wgt4(i,j,k,l) which are each centered at the coordinates (i,j)are given by the following equation (30) (step S23): $\begin{matrix}{{{wgt4}\left( {i,j,k,l} \right)} = {\sum\limits_{m = {{5k} - 2}}^{{5k} + 2}\quad {\sum\limits_{n = {{5l} - 2}}^{{5l} + 2}\quad {{wgt3}\left( {i,j,m,n} \right)}}}} & (30)\end{matrix}$

where Σ_(m=5k−2) ^(5k+2)Σ_(n=5l−2) ^(5l+2) represents the total sum of5×5 elements for m of from 5k−2 to 5k+2 and n of from 5l−2 to 5l+2; inother words, since k and l each range from −1 to 1, m and n are eachcalculated to range from −7 to 7 and, hence, 15×15 elements are simplydivided into 9 sections each consisting of 5×5 elements and all elementsof these 9 section are added together to give Σ_(m=5k−2)^(5k+2)Σ_(n=5l−2) ^(5l+2). In order to construct the adjusted 3×3weighting matrix wgt5′ which allows for even smoother connection betweenpixels than the adjusted 3×3 weighting matrix wgt2′, the 3×3 weightingmatrix wgt4 is normalized to construct a 3×3 weighting matrix wgt5,which is then adjusted by the coefficient of emphasis of moiré intensityα.

The first thing to do is to construct 43×43 units 3×3 weighting matrixwgt5 in accordance with the following equation (31) (step S24):

wgt5(i,j,k,l)=wgt4(i,j,k,l)/ss(i,j)  (31)

where (k,l) represents the amount of shift from the center of a filteron the 400 dpi space, where k and l each range from −1 to 1 and ss(i,j)is equal to Σ_(k)Σ_(l){wgt4(i,j,k,l)}.

Using the thus obtained 3×3 weighting matrix wgt5(i,j,k,l) and adjustingit by the coefficient of emphasis of moiré intensity α in accordancewith the equation (1), one can construct the adjusted weighting matrixwgt5′(i,j,k,l) in accordance with the following equation (28) whichcorresponds to the equation (28) (step S20):

wgt5′(i,j,k,l)=α{−E(k,l) +wgt5(i,j,k,l)}+E(k,l)  (32)

The image data Ca(i,j) obtained by weighted averaging in accordance withequation (21) using the adjusted 3×3 weighting matrix wgt5′ areconverted to RGB image data by the predicting and processing block 23 or24 as shown in FIG. 1 and the resulting RGB image data are supplied intothe printer 17 for producing a hard proof HP or into the display 18 forproducing a soft proof SP. This provides an output image in whichadjacent pixels at the resolution of 400 dpi connect more smoothly,which has no frequency components from periods higher than 400 dpi andin which moiré, line discontinuities and other image fine structuresthat would appear in a printed document are faithfully simulated withtheir intensity adjusted in accordance with the type of the printingmachine to be used to produce the printed document.

However, when the image formed either from the printer 17 or on thedisplay 18 using the 3×3 weighting matrix wgt5′ which was constructedusing the Gaussian filter Gauss2(m,n) and which was subsequentlyadjusted in moiré intensity was observed, the sharpness for each pixelwas found to be inferior to what would be obtained on a printeddocument.

The present inventors found that there were two major reasons for thedecrease in sharpness; one is of course the use of the Gaussian filterGauss2(m,n); the second and more important reason is that when asublimation-type thermal transfer printer or a printer operating onphotography, which are two specific versions of the continuous-toneprinter 17 operating on a density gradient process, are used to outputan image as a proof, these printers are usually adapted to be such thatthe contour of marked dots (pixels) has a Gaussian blur to provide abetter connection between adjacent pixels and to prevent the occurrenceof moiré in the marked dots.

Therefore, with a view to providing image sharpness that is approximateto that of a printed document, the 3×3 weighting matrix wgt4 expressedby equation (31) which is yet to be normalized with ss(i,j) to producethe yet to be adjusted 3×3 weighting matrix wgt5 is processed witheither a space emphasizing filter (also called “sharpness matrix” or“sharpness filter”) which has opposite characteristics to those ofGauss2 or a space emphasizing filter having stronger characteristicsthan said reverse sharpness filter. The method by which image data I′with emphasized image fine structures can be constructed by adopting theprocessing with such space emphasizing filters is described below.

FIG. 14A shows an exemplary composition of a sharpness filter Shp=Shp1which is a “reverse” filter having opposite characteristics to those ofGauss2.

FIG. 14B shows an exemplary composition of a sharpness filter Shp=Shp2which has stronger characteristics than the sharpness filter Shp1.

FIG. 14C shows an exemplary composition of a sharpness filter Shp=Shp3which has stronger characteristics than the sharpness filter Shp2.

For each of the filters Shp1, Shp2 and Shp3, the horizontal axis isrepresented by i, k, r and the vertical axis by j, l, s for providing agood perspective in the use of the equations to be set forth below. Therespective coordinates are on the same system as the coordinate system(i,j) on the 400 dpi space. It should also be noted that for each of thefilters Shp1, Shp2 and Shp3, the total sum of the elements is at unity.

If the image data created by weighted averaging with the 3×3 weightingmatrix wgt5′ which is obtained by performing weight adjustment on thenormalized 3×3 weighting matrix wgt4 with the coefficient of emphasis ofmoiré intensity is written as Ca [see equation (21)] and if the finalimage data created by allowing the sharpness filter Shp to act on Ca iswritten as Cb(i,j), this final image data Cb(i,j) can be obtained by thefollowing equation (33):

Cb(i,j)=Σ_(k)Σ_(l) {Ca(i+k, j+l)×Shp(k,l)}  (33)

where Σ_(k)Σ_(l) is the total sum of the elements for the range of k andl from −1 to 1, and Shp(k,l) is either one of the sharpness filtersshown in FIGS. 14A, 14B and 14C.

Instead of determining the final image data Cb(i,j) from Ca(i,j), theweighting matrix wgt4 may be directly used to determine Ca(i,j) with theaid of an adjusted filter that has been processed by sharpening (step S6in the flow chart shown in FIG. 2) and adjustment of moiré intensity(step S3) and this alternative procedure will allow simplicity incalculations. In order to perform this, it suffices that the process bythe equation (21) and the process by the equation (33) are performed inthe process. To this end, a 3×3 weighting matrix wgt7 for weightedaveraging is first constructed from the combination of a weightingmatrix and a sharpness filter and then adjustment is made by thecoefficient of emphasis of moiré intensity α so as to construct anadjusted 3×3 weighting matrix wgt7′.

For constructing the weighting matrix wgt7, the sharpness filter Shp isfirst applied to the weighting matrix wgt4 to construct a weightingmatrix wgt6 (step S25). The weighting matrix wgt6(i,j,k,l) can beobtained by the following equation (34): $\begin{matrix}{{{wgt6}\left( {i,j,k,l} \right)} = {\sum\limits_{r = {- 1}}^{1}\quad {\sum\limits_{s = {- 1}}^{1}\quad \left\{ {{{Shp}\left( {r,s} \right)} \times {{wgt4}\left( {{i + r},{j + s},{k - r},{l - s}} \right)}} \right\}}}} & (34)\end{matrix}$

where wgt4(i+r, j+s, k−r, l−s) is regarded as zero if k−r and l−s areeach outside the range of from −1 to 1, and Shp(r,s) is either one ofthe sharpness filters shown in FIGS. 14A, 14B and 14C.

In the next step, the wgt6(i,j,k,l) is normalized to construct the 3×3weighting matrix wgt7 (step S26) in accordance with the followingequation (35):

wgt7(i,j,k,l)=wgt6(i,j,k,l)/ss(i,j)  (35)

where k, l=−1, 0, 1, and ss(i,j)=Σ_(k)Σ_(l){wgt6(i,j,k,l)} (where k andl each range from −1 to 1).

Using the thus obtained 3×3 weighting matrix wgt7(i,j,k,l) and adjustingit by the coefficient of emphasis of moiré intensity α in accordancewith the equation (1), one can construct the adjusted 3×3 weightingmatrix wgt7′(i,j,k,l) for weighted averaging in accordance with thefollowing equation (36) which corresponds to the equation (20) (stepS20)

wgt7′(i,j,k,l)=α{−E(k,l) +wgt7(i,j,k,l)+E(k,l)  (36)

Using the thus obtained 3×3 weighting matrix wgt7′(i,j,k,l) as theweight function Weight of equation (21), one can ensure that sharpeningand the simulation of image fine structures with the moiré intensityadjusted properly are accomplished simultaneously by one mathematicaloperation. The image data Cb(i,j) obtained by weighted averaging usingthe 3×3 weighting matrix wgt7′ are converted to RGB image data by thepredicting and processing block 23 or 24 and the resulting RGB imagedata are supplied into the printer 17 for producing a hard proof HP orinto the display 18 for producing a soft proof SP. This provides animage in which moiré, line discontinuities and other image finestructures that would appear in a printed document are simulatedaccurately with their intensity being adjusted in accordance with thetype of the printing machine which is to be eventually used to producethe printed document. The image also reproduces the sharpness of theimage on the printed document. As a further advantage, the weightedaveraging process has no effects on the prediction of colors.

As already mentioned, the intensity of sharpness varies with thespecifications of the printing machine to be used even if the originalimage and the printing conditions (screen ruling, screen angle and printdensity) are the same. The difference defines general prediction sinceit originates not only from mechanical factors such as the printingpressure applied by the printing machine but also from other factorsassociated with software (e.g. halftoning algorithms) and/or hardware.To cope with this problem, the method of the invention uses a means ofinputting the intensity of sharpness of an image which simulates moiréand other image fine structures so that the intensity of sharpness ofthe proof being output is adjusted in accordance with the type of theprinting machine to be eventually used to produce a printed document.

The method to be employed in the present invention for adjusting theintensity of sharpness is by adjusting the intensity of a sharpnessfilter of a size of about 3×3 or 5×5 for use in a conventional sharpnessfiltering, for example, the intensity of an unsharpness mask (USM) whichis employed in an USM process. If the USM such as either one of thesharpness filters Shp1, Shp2 and Shp3 illustrated in FIGS. 14A, 14B and14C, respectively, is written as U(k,l), the USM (filter) afterintensity adjustment as U′(k,l) and the ratio of sharpness emphasis asβ, the filter after sharpness intensity adjustment can be determined bythe following equation (37) (step S27):

U′(k,l)={−E(k,l)+U(k,l)}+E(k,l)  (37)

where (k,l) represents the coordinates of a filter and k and l eachrange from −1 to 1 if U(k,l) and U′(k,l) are each assumed to be a 3×3sharpness filter; E(k,l) is, as already mentioned hereinabove, acoefficient matrix which takes the value “1.0” at its center and thevalue “0.0” in the other positions.

As in the processing by equation (33), the intensity adjusted sharpnessfilter U′(k,l) may be applied to the image data Ca(i,j) resulting fromweighted averaging with the adjusted weighting matrix, whereby the finalimage data are determined. However, more preferably, the followingalternative method may be adopted: the intensity adjusted sharpnessfilter U′(k,l) is incorporated into the position dependent filterF(i,j,k,l) or F′(i,j,k,l) specifically exemplified by wgt0, wgt0′, . . .wgt4, wgt4′, wgt5 or wgt5′, and finally, the sharpness filter U′(k,l)adjusted in sharpness intensity and the position dependent filterF′(i,j,k,l) such as wgt5′ which has been adjusted in moiré intensity areassembled into a single weighted averaging filter and the resultingcomposite filter is used to convert the pixels under calculation.

An example of the construction of the composite filter is describedbelow. The sharpness filter U′(k,l) obtained in step S27 with itsintensity adjusted properly and the previously determined 3×3 weightingmatrix wgt4(i,j,k,l) are assembled by the same procedure as representedby equation (34), whereby a 3×3 weighting matrix wgt8(i,j,k,l) isconstructed in accordance with the following equation (38) (step S28):$\begin{matrix}{{{wgt8}\left( {i,j,k,l} \right)} = {\sum\limits_{r = {- 1}}^{1}\quad {\sum\limits_{s = {- 1}}^{1}\left\{ {{U^{\prime}\left( {r,s} \right)} \times {{wgt4}\left( {{i + r},{j + s},{k - r},{l - s}} \right)}} \right\}}}} & (38)\end{matrix}$

where wgt4(i+r, j+s, k−r, l−s) is regarded as zero if k−r and l−s areeach outside the range from −1 to 1.

In the next step, wgt8(i,j,k,l) is normalized by the same procedure asrepresented by equation (35) so that a 3×3 weighting matrix wgt9 isconstructed in accordance with the following equation (39) (step S29):

wgt9(i,j,k,l)=wgt8(i,j,k,l)/ss(i,j)  (39)

where k, l=−1, 0, 1, and ss(i,j)=Σ_(k)Σ_(l){wgt8(i,j,k,l)} (where k andl each range from −1 to 1).

Using the thus obtained 3×3 weighting matrix wgt9(i,j,k,l) and adjustingit with the coefficient of emphasis of moiré intensity α in the samemanner expressed by equation (36), one can construct an adjusted 3×3weighting matrix wgt9′(i,j,k,l) for weighted averaging in accordancewith the following equation (40) (step S20):

wgt9′(i,j,k,l)={−E(k,l) +wgt9(i,j,k,l)}+E(k,l)  (40)

Using the thus obtained 3×3 weighting matrix wgt9′(i,j,k,l) as theweight function weight of equation (21), one can ensure that asharpening process with the intensity of sharpness adjusted properly andthe simulation of image fine structures with the moiré intensity alsoadjusted properly are accomplished simultaneously by one mathematicaloperation. The image data Cb(i,j) obtained by weighted averaging usingthe 3×3 weighting matrix wgt9′ are converted to RGB image data by thepredicting and processing block 23 or 24 and the resulting RGB imagedata are supplied into the printer 17 for producing a hard proof HP orinto the display 18 for producing a soft proof SP. This provides animage in which moiré, line discontinuities and other image finestructures that would appear in a printed document are simulatedaccurately with their intensity being adjusted in accordance with thetype of the printing machine which is to be eventually used to producethe printed document. The image also reproduces the sharpness of theimage on the printed document. As a further advantage, the weightedaveraging process has no effects on the prediction of colors.

Using the weighting matrix wgt9′ which was adjusted in moiré andsharpness intensities in accordance with the type of the printingmachine to be eventually used, it took about 2 minutes to produce imagesC′M′Y′K′ with emphasized image fine structures from original images CMYKof size A4 at the resolution of 400 dpi. Computer 12 was a commercialpersonal computer manufactured by Apple Computer, Inc. and named PowerMacintosh 9500/132 Series (CPU: Power PC, 132 MHz; memory: 32 MB). Theweighting matrix wgt9′ consisted of 43×43 cells of 3×3 weighting matrixof 400 dpi corresponding to a rational number screen of 2000 dpicomposed of 215×215 elements and it was a weighting matrix for use inthe case where image fine structures on the C (cyan) plate having ascreen ruling of 175 and a screen angle of 75 degrees were simulated onthe printer 17 of 400 dpi; each weighting matrix was used aftermultiplying the respective coefficients by a factor of 8192 so thattheir normalized values would be integers.

In the embodiment under consideration, the sharpness and moiréintensities are preferably adjusted in the following manner.

When the coefficient of emphasis of moiré intensity α is set between 60and 180% (0.6-1.8) during simulation, substantial changes in moiréintensity are visually verified on proofs. With ordinary printeddocuments, close resemblance in appearance is achieved when α is set toabout 120%, so 60-180% is adequate for dealing with most printeddocuments; hence, in the case under consideration, the coefficient ofemphasis of moiré intensity α is preferably in the range between 0.6 and1.8.

As for the emphasis of sharpness, it has already been pointed out thatthe image obtained from a continuous-tone printer is somewhat blurredsince the dots marked with the printer have a spatial distributionresembling a Gaussian function and that the image in a printed documentproduces a sharp visual impression on account of the fact it is composedof tiny dots. Therefore, in order to correct this difficulty, sharpnessmust deliberately be provided with respect to the actual printeddocument; otherwise, the proof cannot have the same level of apparentsharpness as the printed document. When proofs simulating a printeddocument having a screen ruling of 175 are produced on Pictrography 3000(continuous-tone printer of 400 dpi manufactured by Fuji Photo Film Co.,Ltd.) using a 5×5 USM mask, substantial changes in sharpness can bevisually observed at β values of 0-80% and a sharpness close to that ofthe printed document is obtained at about 50%. Using levels of 0-80%,one can produce proofs adequate for most of ordinary printed documents;hence, in the case under consideration, the ratio of sharpness emphasisβ is preferably in the range between 0.0 and 0.8.

An exemplary method of using the image fine structure predicting system11 having the image fine structure predicting and processing block 22with the structural features described above will now be described belowwith reference to FIG. 15.

To begin with, the screen attribute input device 14 is activated todesignate the screen ruling LPI and screen angle θ, the moiré intensityinput device 15 is activated to designate the coefficient of emphasis ofmoiré intensity α, and the sharpness intensity input device 16 isactivated to designate the ratio of emphasis of sharpness intensity β(step S31).

Subsequently, matrices for the four plates CMYK (i.e., image finestructures emphasizing filters) are determined on the basis of thescreen ruling LPI and screen angle θ so as to produce image finestructure emphasizing filters in which the intensity of a positiondependent filter is adjusted by the coefficient of emphasis of moiréintensity α and the intensity of a sharpness filter adjusted by theratio of emphasis of sharpness intensity β (step S32). In theillustrated case, wgt9′ shown in FIG. 6 is used as the weighting matrixbut the present invention permits the use of any one of the followingadjusted weighting matrices shown in FIG. 6, wgt0′, wgt1′, wgt5′ andwgt7′. Such weighting matrix is allowed to act on the respective platesCMYK, thereby emphasizing the image fine structures (step S33).

Image data I′ with emphasized image fine structures are converted intodisplay's RGB image data by means of the display color predicting andprocessing block 24 (step S34).

The four plates CMYK based are assembled based on the display's RGBimage data and the resulting image fine structures are examined on thescreen of the display 18 (step S35).

If necessary, the image on either one of the plates C, M, Y and K isexamined by monitoring on the display 18 (step S36).

Subsequently, the result of examination is checked to see if it issatisfactory or not (step S37). If the result is unsatisfactory due tothe appearance of moiré or any other image fine structures in the imageon the display 18, the process returns to step S31, the screen rulingLPI and screen angle θ are designated again and the steps up to S36 arerepeated.

If the result is satisfactory since moiré, line discontinuities or anyother image fine structures do not appear in the image, the CMYK imagedata Ia′ with emphasized image fine structures which have been obtainedin step S33 are converted to printer's RGB image data by means of theprint color predicting and processing block 23 (step S38) and a hardproof HP is output from the printer 17 for use in proof reading (stepS39).

According to the embodiment described above, a proof depicting andsimulating the image fine structures which will appear in an actualprinted document can be produced within a much shorter processing timethan in the prior art, for example, a proof with an image of size A4 canbe produced by processing for about two minutes (excluding the printtime) by means of the above-mentioned commercial personal computer.

Therefore, by making use of the image fine structure predicting system11 shown in FIG. 1, printing conditions (screen ruling and angle) thatwill eliminate moiré, line discontinuities and other image finestructures can be designated in accordance with the type of a specificprinting machine at the site of image capture with the image inputdevice 13 such as a scanner, whereby satisfactory prediction can be madeas to the finish in terms of image fine structures. This ensures thatthe quality of printed documents up to the final printing stage can beguaranteed even at the “site of color separation” although this has beenimpossible to accomplish by the prior art.

In addition, even if expensive halftoning printers or any specialmaterials (chemical proofs) are not used, proofs that can simulate imagefine structures which will occur due to the halftoning process can beproduced from a computer to which an inexpensive printer (of low imageresolution, density resolution and operating stability) is connected.

As a further advantage, the image data having the image fine structuresemphasized in the embodiment described above are obtained by a weightedaveraging process and, hence, will cause no effects on colors.Therefore, if such image data are used on a color predicting printer,proofs are produced that enable the prediction of not only colors butalso image fine structures.

In association with image fine structures, the following four items canbe checked by examining the proofs produced in accordance with theabove-described embodiment of the invention after making properadjustments on the habit and other characteristics of the printingmachine to be eventually used to produce a printed document: 1) thepresence or absence of moiré, the intensity of moiré and theidentification of a plate or plates that are suspected of causing moiré;2) whether line discontinuities will occur when fine lines in theoriginal image are printed on the printing machine; 3) whetherimperfections in straight lines will take place on account of thehalftoning process; and 4) sharpness of the image in the printeddocument.

The proofs also allow for the checking of the overall impression of thehalftone created in the printed document when the results of checkingfor parameters 1)-4) are combined together.

As described above in detail, the first embodiment of the presentinvention is characterized in that when converting the values of pixelsin the original image on each of the CMY or CMYK plates into pixelvalues for predicting the image fine structures that would appear in animage on a printed document, the values of the pixels of interest andneighboring pixels are subjected to weighted averaging by weightsdependent on the period of grid units which is determined by the screenruling and angle and, hence, the image fine structures which wouldappear in the printed document can be predicted on the proof image whichis based on the image data composed of the converted pixel values. Inother words, moiré, line discontinuities and other image fine structureswhich would appear on the printed document can be predicted on hard orsoft proofs in an accurate and easy manner within a short period oftime.

If a sharpness filter is allowed to act upon the image produced byweighted averaging, moiré, line discontinuities, imperfections instraight lines and other image fine structures that would appear in aprinted document can be predicted on hard or soft proofs in an accurateand easy manner without impairing image sharpness.

The method of the invention which performs the weighted averagingprocess has the advantage of causing no side effects on colors.Therefore, if the CMYK data in the original image to be printed areconverted to RGB data for use by a continuous-tone printer which iscapable of producing colors very close to those computed by the weightedaveraging process performed on said CMYK data, one can construct a proofthat is highly approximate to the printed document not only in terms ofmoiré, line discontinuities, imperfections in straight lines and otherimage fine structures but also with respect to colors.

In addition, according to the method of the invention, an inexpensivemachine such as a continuous-tone printer can be used to provideconvenience-in producing a proof that simulates the image finestructures which will develop due to printed halftone dots. A particularadvantage is that even a continuous-tone printer of low resolution(e.g., on the order of 200 dpi as is typically the case of a thermaltransfer-type printer) can accomplish moiré simulation by adopting themethod of the invention.

The present invention enables high-speed simulation of moiré and otherimage fine structures.

What is more, the method of the invention allows for adjustment of theintensities of moiré and sharpness on simulations (proofs) of image finestructures and, hence, sharpness and moiré can be simulated in a mannerthat is appropriate for the specific type of a printing machine to beused, thereby making it possible to produce proofs having higherfidelity to the printed document.

In the prior art, moiré cannot be displayed on a monitor unless theimage of interest is enlarged to the screen resolution (≧1000 dpi);however, the magnification necessary to meet this requirement is so highthat it has been practically impossible to locate the area where moiréappears. This is not the case of the present invention and, given animage size of A4, the entire range of moiré occurrence can be examinedby scrolling the viewing screen.

On the pages that follow, we will next describe a method of predictingand processing image fine structures in accordance with the secondembodiment of the invention with reference to FIGS. 16 -22.

FIG. 16 is a flow chart showing a system of producing color proofs byimplementing the second embodiment of the invention method of predictingand processing image fine structures, as well as the basic flow of acommon color printing system. Those parts or steps shown in FIGS. 16-22which correspond to those already described in connection with the priorart method of producing color proofs which is shown in FIG. 23 areidentified by like numerals or symbols and will not be described belowin detail unless required by special need.

Before describing a color proof producing system 60 for implementing theinvention method of predicting and processing image fine structures, letus first provide a description of a color printing system 61 whichgenerally uses a color printing machine to produce color printeddocuments.

In FIG. 16, reference numeral 61 signifies the composition of a commoncolor printing system, in which the image on an image document 52 isread two-dimensionally with an image reader such as a color scannerhaving a CCD linear image sensor and gradation image data Ia aregenerated for each of the colors R (red), G (green) and B (blue) in stepS51 (image reading step). The image sensor such as a CCD linear imagesensor has a resolution (first resolution) Re1, which may be selected atabout 400 dpi (dots per inch). One dot corresponds to one pixel, withpixels being generated by a density gradation process to producemultiple (e.g. 256) gradations.

The pixel data composing-the RGB gradation image data Ia are thenrendered by a color conversion process using conversion lookup tables orthe like into dot area percentage data aj (also called “dot percentagedata” or “original image pixel percentage data” as already noted inconnection with the first embodiment) for the four plates of respectivecolors C (cyan), M (magenta), Y (yellow) and K (black) in step S52(color conversion step). The color conversion process allows for variousversions in relation to the color printing machine to be described lateron and it is usually the proprietary know-how of individual printingcompanies who employ different color printing machines. If UCR(undercolor removal) process is not to be performed, the RGB gradationimage data Ia may be converted to dot area percentage data aj for thethree plates C, M and Y. Needless to say, if Y color is not to bereproduced on a color printed document 62, the RGB gradation image dataIa suffices to be converted to dot area percentage data aj for the twoplates C and M.

After step S52, the dot area percentage data aj for the four plates CMYKwhich were generated pixel for pixel are converted into bilevel data(taking either “0” or “1”), or bit map data bj, by comparing therespective threshold values in the elements of four printing thresholdmatrices 64 (also called “threshold templates” or “dot templates”) withthe values of the dot area percentage data aj by means of a comparator(not shown), with reference being made to said threshold matrices whichhave a resolution (second resolution) Re2 of about 2000 dpi (for easyunderstanding, Re2=1600 dpi in the embodiment under discussion) andwhich have specified screen angles for the four plates CMYK (step S55:comparison step). Usually, the respective threshold matrices 64 havedifferences in screen angle, as exemplified by a 45° difference betweenthe threshold matrix for Y plate and that for M plate. In practice, thefour plates CMYK have screen angle differences of 75°, 45°, 0° and 15°with respect to the reference (provided by plate Y).

FIG. 17 is a diagram which provides a schematic representation ofthreshold matrices 64 and so forth in order to give details about thecomparison to be made in step S55 (for generating bit map data). The twodiagrams in the top of FIG. 17 illustrate the conversion of one dot inthe dot area percentage data aj of 400 dpi into 16 dots in the bit mapdata bj of 1600 dpi.

Suppose here that one dot in the dot area percentage data aj belongs toplate C and also suppose that the dot area percentage data aj which arerepresented in 256 gradations assume a value of 77 (this corresponds to30% but in the case of comparison process, aj is usually represented ingradations and takes an integral value such as 77); then, this value ofaj is compared with the threshold matrix 64 for C plate. The thresholdmatrix 64 is typically composed of threshold values T arrangedconvolutionally in the matrix elements as shown in FIG. 17. Thethreshold matrix 64 has no direct bearing on the present invention, soit will not be described here in any greater details; it suffices hereto say that the threshold matrix 64 is a hypothetical entity which isreconstructed from an extracted portion of either threshold values Tcorresponding to one dot, said threshold values taking 8-bit gradations(0, 1, 2, . . . 254 and 255) and being arranged convolutionally from thecenter outward, or a supercell (e.g. one threshold corresponding to 9dots).

Further referring to FIG. 17, generation of bit map data bj, orconversion of the values of dot area percentage data aj into bileveldata, is carried out by a well-known technique in accordance with thefollowing equations (41) and (42):

aj≧T→1  (41)

aj<T→0  (42)

Thus, as shown in the bottom of FIG. 17, bit map data bj are generatedwhich correspond to one pixel of interest in plate C (i.e., the pixelfor which the dot area percentage data aj is 30%). As already mentioned,the threshold matrices 64 for plates M, Y and K have screen anglesselectable with respect to the threshold matrix 64 for plate C.

On the basis of the thus generated bit map data bj, the necessaryprocesses are performed with a photocomposing machine, an automaticdeveloping machine, and so forth in step S56 (platemaking step) so as toprepare four process-plate films 66 having halftone images and servingas camera-ready copies, as well as PS plates 67 (also called “pressplates”).

Finally, using the press plates 67, a printed color document 62 composedof a halftone image is produced with a color printing machine havingrotary presses in step S57 (printing step).

The halftone image on the printed color document 62 has moiré, a rosetteimage and other image fine structures which appear on account of the useof threshold matrices 64 having different screen angles.

This is how the color printing system is composed for producing colorprinted documents using a common printing machine.

We next describe the color proof producing system 60 implementing theinvention method of predicting and processing image fine structures.

The color proof producing system shown in FIG. 16 (which is hereinafterreferred to simply as “proof system”) first requires that aposition-dependent six-dimensional lookup table (hereafter abbreviatedas “6D-LUT”) for converting the CMYK dot area percentage data aj at aresolution of, say, 400 dpi into common color space data for predictinga rosette and other image fine structures with a continuous-toneprinter, such as tristimulus data XYZ at a resolution of, say, 400 dpishould be prepared in advance in step S58 (6D-LUT preparing step). Theproof system 60 then starts to produce printed color proofs through animage reading step (S51) and a color conversion step (S52); it sharesboth of these steps with the color printing system 61.

After the color conversion process S52, the dot area percentage data aj(400 dpi) are processed for prediction of (first) image fine structuressuch as moiré, whereby the amplitude of the image fine structures to bepredicted is adjusted in step S59 (first image fine structure predictingand processing step, which is hereinafter also referred to as “moiréemphasizing step”; see the description of the method of predicting andprocessing image fine structures in accordance with the first embodimentof the invention). From step S59, there are outputted dot areapercentage data a′j (having a resolution of, say, 400 dpi) which areemphasized in moiré and other first image fine structures and these dataa′j are subjected to a process of predicting (second) image finestructures such as a rosette image by interpolation and like techniquesusing the position-dependent 6D-LUT, whereby a′j are converted totristimulus data XYZ (400 dpi) on a common color space which areadjusted in the amplitude of the image fine structures to be predicted.This is step S60, or a second image fine structure predicting andprocessing step (which is hereinafter sometimes referred to as “rosetteemphasizing step” or “6D-LUT processing step” as the case may be).

Finally, the tristimulus data XYZ (400 dpi) from the rosette emphasizingstep S60 which have been emphasized in first image fine structures (e.g.moiré) and second image fine structures (e.g. rosette image) aresupplied into a continuous-tone printer (digital color printer DP) 53,where they are converted to output device (DP 53) dependent threeprimary RBG data (having a resolution of, say, 400 dpi) on the basis ofa color conversion lookup table (not shown) or the like (step S61, orcolor conversion process). On the basis of the color converted threeprimary data having the output resolution (400 dpi), DP 53 outputs acolor proof CPb.

The thus obtained color proof CPb not only provides a faithfulreproduction of moiré, rosette and other image fine structures thatwould appear in the printed color document 62 produced with the colorprinting machine but also reproduces colors that match the colors in theprinted document; therefore, CPb is an accurate proof of the printedcolor document.

The preparation of 6D-LUT in step S58 is the most characterizing portionof the second embodiment of the invention. The present inventors alreadyproposed in Unexamined Published Japanese Patent Application (kokai) Hei8-192540 a method of producing color proofs which takes much time inprocessing but which is capable of accurate simulation of image finestructures that would appear in printed documents. According to thesecond embodiment of the present invention, the mathematical operationsfor predicting image fine structures that are performed in the proposedmethod are applied to representative colors and the results of suchoperations are stored in the position-dependent 6D-LUT; in the actualpreparation of a color proof, said mathematical operations are notperformed but the position-dependent 6D-LUT is applied to the halftoneimage (area percentage) data obtained from the original image and thenecessary interpolation is effected to ensure the same result as will beobtained by performing said mathematical operations on those halftoneimage data from the original image. As a consequence, the calculationtime that is actually taken by the method of the present invention is nolonger than what is required by interpolation from 6D-LUT and, hence, asubstantial time reduction is realized in comparison with the dataprocessing technology for depicting image fine structures as proposed inUnexamined Published Japanese Patent Application (kokai) Hei 8-192540(which technology is hereinafter referred to simply as “basic (data)processing technology”).

Therefore, it is necessary for the method of the invention to prepare6D-LUT prior to the production of a color proof from the original image.FIG. 18 shows an example of the flow of the preparation of 6D-LUT instep S58 according to the characterizing feature of the invention. Thefirst step of the flow is to determine representative colors to whichthe basic data processing technology is to be applied: n colors (plates)including at least three primaries are set; in the case underconsideration, four (n=4) colors (plates) CMYK are set and each isdivided in N stages, for example, 6 stages, whereby a total of N^(n)colors are determined (in the illustrated case, 6⁴=1296 colors aredetermined). This is step S62 for determining representative colors.Since the image data on which the 6D-LUT is allowed to act are dot areapercentage data aj (%), the dot area percentage data for the respectiveN stages of each of the four colors CMYK consist of 0%, 100/(N−1),2×100/(N−1), . . . , (N−2)×100/(N−1) and 100%; if N=6, the correspondingdata are 0%, 20%, 40%, 60%, 80% and 100%. The following descriptionassumes that each of colors (plates) CMYK is divided into 6 stages.

In the method of the invention, a rational number screen is used in thehalftoning step in the basic data processing technology. This is becausea rational number screen ensures that a repeated pattern of second imagefine structures such as a rosette image can be rendered into therepetition of m_(r)×n_(r) rectangular blocks (pixels) in the rationalnumber screen as in the case of simulating first image fine structuressuch as moiré. Symbols m_(r) and n_(r) represent the size of therational number screen in terms of a certain resolution, say, 400 dpi.As a result, recording m_(r)×n_(r) data files (m_(r)×n_(r) representingthe repeated screen unit size or pixel size) enables the shape of arosette image to be represented for the screens on all spaces. This ishow the size of 6D-LUT is determined.

Accordingly, the basic data processing technology is applied to N^(n)(=1296) monochromatic images each having a size comparable tom_(r)×n_(r) rectangles such that the halftoning process is performedusing a screen having a period of m_(r)×n_(r). To give a specificexample, if a printed document having a halftone image created with ascreen ruling of 175 using Y, C, M and K plates having respective screenangles of 0, 15, 45 and 75 degrees is to be simulated on acontinuous-tone printer of 400 dpi, the required repeating screen unitsize (m_(r)×n_(r)) is 43×43.

The six-dimensional lookup table (6D-LUT) prepared by theabove-described procedure has the following parameters as arguments: thecoordinates (x,y) of the positions of the pixels in the 1296monochromatic original images (x between 0 and m_(r)−1 and y between 0and n_(r)−1) and the number of stages (or steps) of each of the colorsCMYK; hence, 6D-LUT may be expressed as 6DLUT[i] [j] [k] [y] [m] [c] andrelevant color data, for example, tristimulus data XYZ are assigned torespective values of this expression.

In the stated expression, i=0-42, j=0-42, c=0-5, m=0-5, y=0-5, andk=0-5, i representing the x coordinate of the original image, jrepresenting the y coordinate of the original image, and k, y, m and crepresenting the number of steps of the plates K, Y, M and C,respectively; step numbers of 0, 1, 2, 3, 4 and 5 correspond to 0%, 20%,40%, 60%, 80% and 100%, respectively. It should be noted here thatassuming N gradation levels, the M_(N)th step is generally expressed byM×100/(N−1) (%) (where M_(N) is between 0 and N−1).

In order to determine tristimulus data XYZ corresponding to 6DLUT[i] [j][k] [y] [m] [c], the basic data processing technology to be describedbelow (for details, see Unexamined Published Japanese Patent ApplicationHei 8-192540) is applied to one of the 1296 monochromatic images,thereby providing tristimulus data XYZ for the coordinates of thepositions of 43×43 corresponding pixels at a repeating size of 43×43.

For better understanding of the basic data processing technology, let usfirst describe an anti-aliasing filtering process shown as step S67 inFIG. 18.

When a color proof CPb is produced at a resolution of Re3 (being theresolution of DP 53, Re3 is also called the “third resolution” or“output resolution and set at 400 dpi in the case under consideration),aliasing noise will occur on account of Re3. The anti-aliasing filteringprocess is inserted to preclude the generation of such aliasing noise.For effective performance of the anti-aliasing filtering process, theresolution (fifth resolution or Re5) of the image data which is theoriginal signal to be processed with an anti-aliasing filter must behigher than Re3 (=400 dpi) which is the resolution of DP 53. In the caseunder consideration, Re5 or the fifth resolution (which may also becalled the “intermediate resolution”) is set at 1600 dpi.

The matrix of the anti-aliasing filter (which is a square matrix of n×nelements) is first considered. If the image data having a resolution of1600 dpi (=Re5) are to be converted to image data having a resolution of400 dpi (=Re3), a minimum number of elements in a filter having noanti-aliasing capability is 4×4 since one dot (pixel) of 400 dpicorresponds to 16 dots (4×4 pixels) of 1600 dpi.

For minimizing the aliasing noise, the anti-aliasing filter desirablyhas the greatest number of elements but limiting factors are thecomputing speed, hardware and so forth.

As will be understood by analogy from the reproducibility of colorinformation by the Naugebauer's equation, the anti-aliasing filter musthave such frequency characteristics that the smallest possible insertionloss will occur at frequencies in the neighborhood of the dc componentand this is in order to satisfy the need for passing comparativelylow-frequency components including the dc component. Therefore, theanti-aliasing filter should ideally have a response of 0 dB at thecenter of the matrix.

On the other hand, interference fringe components such as moiré [i.e.,those components having a frequency not higher than one half the screenfrequency (screen ruling) component] should altogether remain intactafter the anti-aliasing filtering process.

In addition, if the anti-aliasing filter has sharp attenuationcharacteristics, a new peculiar pattern will appear after theanti-aliasing filtering process. This possibility must also beconsidered in designing the anti-aliasing filter.

FIG. 19 shows the structure of an anti-aliasing filter AF composed of9×9 elements which is constructed incorporating all of theabove-mentioned design considerations. If the elements of the filter AFare represented by d(i,j), the values of the individual elements d(i,j)(also referred to as “filter coefficients”), when added together, mustamount to a total of 1.0. To meet this requirement, the actual value ofeach element d(i,j) is divided by the total sum of the elements d(i,j){=Σ_(i=−4) ⁴Σ_(j=−4) ⁴d(i,j)}. As will be apparent from FIG. 19, thefilter coefficients of the thus composed anti-aliasing filter AF arearranged such that its frequency characteristics provide a bell-shapedattenuation pattern characterized by monotonic decrease from the centeroutward.

FIG. 20 shows the frequency characteristics of the anti-aliasing filterAF. The horizontal axis of the graph in FIG. 20 plots resolution suchthat the resolution of DP 53 (Re3=400 dpi) is standardized to 1.0.Hence, the screen ruling of 175 which represents the screen frequency isstandardized to a value of 0.4 (175/400). The vertical axis of the graphin FIG. 20 plots the response of the filter AF and the value of 121which is assumed by the central element d(5,5) in FIG. 19 isstandardized to 1.0.

As will be understood from FIG. 20, the filter AF shown in FIG. 19 has aresponse of about 0.23 at the resolution of 1.0 and a response of about0.77 at the resolution 0.44.

According to the result of analyses of various cases, an anti-aliasingfilter having a response of at least 0.5 (50%) when the resolution wasequal to the screen frequency (=screen ruling) and a response of no morethan 0.3 (30%) when the resolution was 1.0 (the resolution of colordigital printer DP 53) could ensure that moiré and other peculiarpatterns which would appear in the color printed document 62 werereproduced on the color proof CPb while suppressing any aliasing noiseto a visually unrecognizable level.

The foregoing is the description of the composition of the matrix of theanti-aliasing filter AF, which, in the case under consideration, is asquare matrix consisting of n×n (=9×9) elements.

The anti-aliasing filtering-process in step S67 produces image data(non-device dependent image data) on a common color space at aresolution of 400 dpi (=Re3) which, in the case under consideration, aretristimulus data X, Y, Z (also called “second tristimulus data X′, Y′,Z′”).

The image data (tristimulus data X, Y, Z) which are to be processed withthe anti-aliasing filter AF are set to have a resolution of 1600 dpi andone dot in said image data is not of bilevel data but of image data on acommon color space which, in the case under consideration, istristimulus data X, Y, Z (which may also be called “first tristimulusdata”).

In order to produce the first tristimulus data X, Y, Z without using theNaugebauer's equation, the dot area percentage data aj for 43×43 pixelsin a monochromatic image of one of the 1296 colors produced in therepresentative color determining step S62 are compared with thethreshold values in the threshold matrices 74 (which may also be called“threshold templates” or “dot matrices” by means of a comparator 75 andconverted to bilevel bit map data b′j having a higher resolution thanthe printing bit map data bj (=1600 dpi) (step S63).

The threshold matrices (or templates) 74 for use in step S63 arerational number screen templates. For reproduction of moiré and otherimage fine structures, it is essential that the screen ruling for thethreshold matrices 74 be the same as the screen ruling used in printing.In the case under consideration, the threshold matrices 74 are designedto have a screen ruling of 175. In order to provide high resolution, thethreshold matrices 74 for creating halftone dots each consist of 256×256(=65536) elements. The threshold T in each of the elements may typicallytake either one of the values ranging from 0 to 255. The thus generatedbit map data b′j for the four plates CMYK have a resolution (fourthresolution: Re4) of 44800 (=256×175) dpi.

In order to convert the bit map data b′j of 44800 dpi into the firsttristimulus data X, Y, Z at a resolution (Re5) of 1600 dpi, one mayconvert 28×28 dots in the bit map data b′j into one dot in the firsttristimulus data X, Y, Z. A data processing section 76 is provided foreffecting such conversion (step S64).

Step S64 will be better understood by referring to FIG. 21A which shows28×28 dots in the bit map data b′j for C plate and to FIG. 21B whichshows 28×28 dots in the bit map data b′j for M plate. All elements thatare not shown in FIGS. 21A and 21B are assumed to take value “zero”. Itis also assumed that all elements of the remaining bit map data b′j forY and K plates take value “zero”.

For the 28×28 dots, simultaneous reference is made to the bit map datab′j for the four plates CMYK (needless to say, the bit map data b′j forthe two plates C and M will suffice in the case under consideration) andthe area percentage ci is counted up for each of the colors of interest(which are 24 in number since there are four plates to be processed) ina count-up section 77. This is step S65 (count-up step).

For the pixels (corresponding to 28×28 dots) which are shown in FIGS.21A and 21B, the area percentage ci is calculated as follows for therespective colors:

Color C: ci=c _(c)=3/784

(where c_(c) represents the area percentage of an area where only colorC is present when C and M plates are superposed and viewed undertransmitted light and an area where colors C and M overlap isrepresented by area percentage c_(C+M) of color B (=C+M);

Color C+M: c _(C+M)=2/784

Color W; c _(w)=779/784

(this parameter represents the area percentage of an area where neitherof colors C and M are present when C and M plates are superposed andviewed under transmitted light).

The remaining colors (i.e. 13 colors including Y and K) have an areapercentage ci of zero. Thus, the first tristimulus data X, Y and Z of1600 dpi are generated.

In step S53, colorimetric data Xi, Yi, Zi for respective colors (irepresents 2⁴=16 colors for the four plates CMYK) were preliminarilymeasured by the process described in detail in the Prior Art section ofthis specification (i.e., using a colorimeter to measure 16 solid colorsprinted on the color document 12). After step S65, the colorimetric dataXi, Yi, Zi are subjected to weighted averaging in a section 73 using asweight coefficients those values of area percentage ci which werecounted up color for color in step S65, whereby the first tristimulusdata X, Y, Z which represent mean colorimetric values are determined inaccordance with the following equations (43) (step S66). Briefly, thecolorimetric data Xi, Yi, Zi are subjected to weighted averaging withcolor-dependent values of area percentage ci so as to determine thefirst tristimulus data X, Y, Z: $\begin{matrix}\begin{matrix}{X = \quad {\sum{{ci} \cdot {Xi}}}} \\{= \quad {{\left( {3/784} \right)X_{C}} + {\left( {2/784} \right)X_{C + M}} + {\left( {779/784} \right)X_{W}}}} \\{Y = \quad {\sum{{ci} \cdot {Yi}}}} \\{= \quad {{\left( {3/784} \right)Y_{C}} + {\left( {2/784} \right)Y_{C + M}} + {\left( {779/784} \right)Y_{W}}}} \\{Z = \quad {\sum{{ci} \cdot {Zi}}}} \\{\quad {{\left( {3/784} \right)Z_{C}} + {\left( {2/784} \right)Z_{C + M}} + {\left( {779/784} \right)Z_{W}}}}\end{matrix} & (43)\end{matrix}$

By performing both the count-up process (step S65) and the weightedaveraging process (step S66) for each group of 784 (=28×28) dots overthe entire range of the bit map data b′j, the first tristimulus data X,Y, Z of 1600 dpi are obtained.

The thus obtained first tristimulus data X, Y, Z of 1600 dpi are thenprocessed with the above-described anti-aliasing filter AF to generatethe second tristimulus data X′, Y′, Z′ at 400 dpi which is equal to theresolution of DP 53 (step S67).

FIGS. 22A and 22B are diagrams illustrating how the first tristimulusdata X, Y, Z are processed with the anti-aliasing filter AF. As shown inFIG. 22A, 9×9 dots in the top left portion of the first tristimulus dataX, Y, Z of 1600 dpi which are centered at the pixel element e(5,5) arebrought into correspondence with the anti-aliasing filter AF of a 9×9matrix (see FIG. 19) of which the elements are represented by d(k,l) andall the elements of the matrix are multiplied by the corresponding dotsand the total sum of the products is determined to perform theanti-aliasing filtering process, thereby yielding element e(l,l) of thesecond tristimulus data X′, Y′, Z′ of 1600 dpi. Stated specifically, ifthe position of the central element in the 9×9 dots is written as(i′,j′) and the respective elements of the first tristimulus data X, Y,Z as e(i′,j′,k,l) (where k and l are between −4 and 4 for each of X, Yand Z), then Σ_(l=−4) ⁴Σ⁼⁻⁴ ⁴{d(k,l)×e(i′,j′,k,l) is determined for eachof the first tristimulus values X, Y and Z and the respective values areused as the second tristimulus data X′, Y′, Z′ at a resolution of 400dpi. As already noted, the total sum for the anti-aliasing filter isstandardized to Σ_(l=−4) ⁴Σ_(l=−4) ⁴d(k,l)=1 (k and l are each between−4 and 4). Alternatively, to dispense with the prolonged calculationsthat are necessary to perform multiplications of numbers containingdecimal fractions, the values of the individual elements of theanti-aliasing filter AF may be used unchanged from the data shown inFIG. 19 and written as d′(k,l) so that Σ_(l=−4) ⁴Σ_(L=−4)⁴{d′(k,l)×e(k,)}/Σ⁼⁻⁴ ⁴Σ_(l=−4) ⁴d′(k,l) (k′,=−4˜4) is determined as thevalue obtained by processing with the anti-aliasing filter AF.

The purpose of the anti-aliasing filtering process is to convert thefirst tristimulus data X, Y, Z of 1600 dpi to the second tristimulusdata X′, Y′, Z′ of the lower resolution 400 dpi, so a second cycle ofthe anti-aliasing filtering process may be carried out with theanti-aliasing filter shifted by four dots of the first tristimulus dataX, Y, Z either horizontally to the right as shown in FIG. 22B orvertically downward. By successively applying the anti-aliasing filterAF in this manner, the resolution (1600 dpi) of the first tristimulusdata X, Y, Z can be lowered to produce the second tristimulus data X′,Y′, Z′ of 400 dpi. Suppose here that the individual elements of thesecond tristimulus data X′, Y′, Z′ of 400 dpi are expressed by e′(i+1,j+1), with (i+1, j+1) representing their position coordinates (i and jare each between 0 and 42); also suppose that the elements of a 9×9 dotportion of the first tristimulus data X, Y, Z of 1600 dpi whichcorrespond to said elements and which are to be processed with theanti-aliasing filter AF of a 9×9 matrix are expressed by e(i′,j′,k,l),with (i′,j′) representing the positional coordinates of the centralelement and (k,l) representing the respective positions of the 9×9 dots(k and l are each between −4 and 4); further assume that the individualelements of the anti-aliasing filter AF of a 9×9 matrix are expressed byd(k,l) where k and l are each between −4 and 4. Based on theseassumptions, i′=4i+5 and j′=4j+5 and, hence, the following equation (44)holds:

e′(i+1, j+1)

=Σ_(k=−4) ⁴Σ_(l=−4) ₄ d(k,l)×e(i′,j′k,l)

=Σ_(k=−4) ⁴Σ_(l=−4) ⁴ d(k,l)×e(4i+5, 4j+5, k,l)  (44)

If the individual elements e′(i+1, j+1) of the second tristimulus dataX′, Y′, Z′ are determined by applying the equation (44) to 43×43elements of the repeating units (or repeating size) where i and j eachrange from 0 to 42, a lookup table can be determined for onemonochromatic image (such as one where K=20·k0%, Y=20·y0%, M=20·m0% andC=20·c0%), with the lookup table being typically expressed as 6DLUT[i][j] [k0] [y0] [m0] [c0]. By determining such lookup table for the 1296monochromatic images, one can construct 6DLUT[i] [j] [k] [y] [m] [c],where i and j are each between 0 and 42, and k, y, m and c each between0 and 5.

Thus, tristimulus data XYZ of low resolution (equivalent to the outputresolution of 400 dpi) can be obtained by applying the basic dataprocessing technology of Unexamined Published Japanese PatentApplication (kokai) Hei 8-192540 to 1296 colors, with the calculationsbeing based on the size of m_(r)×n_(r) pixels. The lookup tablesindicated as 6D-LUT 77 in FIG. 18 have been constructed by storing suchtristimulus data XYZ in correspondence with the 1296 colors. If it isdesired to further enhance the precision of colors or adjust thecontrast of rosette and other image fine structures, the 6D-LUT 77 ispreferably subjected to the following two post-processing steps.

As shown in FIG. 18, 6D-LUT is subjected, either direct or afterapplying the second post-processing step to be described later, to colormatching (the first post-processing step S68). In the color matchingstep S68, the average of each of the 1296 colors for 43×43 regions isadjusted to the desired color. To ensure matching of each color to thedesired color, this step S68 shall be performed on all pixels in the43×43 regions for the 1296 colors. Processing in this step is done withall colors (1296 colors) taken as a group and 1296 cycles of processingare performed in succession. The color matching step S68 consists of thefollowing two stages:

(Stage one) The results of application of the 6D-LUT to the respectivecolors are output on a proof printer. The output colors are measuredwith a colorimeter, which outputs Xout, Yout and Zout.

(Stage two) Mathematical operations are performed on the respectivecolors in accordance with the following equations (45) to ensure thateach color as averaged for 43×43 regions matches the desired color. Ifcolors at certain points in the 43×43 regions are written as Xi, Yi andZi (i is between 1 and 43×43), the results of processing as Xi′, Yi′ andZi′ (i is between 1 and 43×43), and the desired colors as Xa, Ya and Za,the following equations (45) will hold:

Xi′=(Xi/Xout)×Xa

Yi′=(Yi/Xout)×Ya

Zi′=(Zi/Xout)×Za  (45)

Substituting the thus obtained tristimulus data X′, Y′, Z′ for thetristimulus data stored in 6D-LUT 77 yields adjusted (color matched)6D-LUT 78. Using the adjusted (color matched) 6D-LUT 78, one can producecolor proofs which are high not only in the-fidelity of reproduction ofmoiré and other image fine structures but also in the precision of colorreproduction.

As also shown in FIG. 18, 6D-LUT is subjected, either direct or afterapplying the first post-processing step, to rosette emphasis step (thesecond post-processing step S69). This is the step of emphasizing thecontrast of image fine structures such as rosette. As in color matchingstep S68, the mathematical operations to be performed in the step S69apply to 43×43 pixels for the 1296 colors and processing, which is donewith all colors taken as a group, is performed through 1296 successivecycles. The rosette emphasis step S69 consists of the following twostages:

(Stage three) Each color is first averaged for the 43×43 regions. If theindividual colors are written as Xi′, Yi′, Zi′ (i represents a pointsomewhere in the 43×43 regions), the averages Xave, Yave and Zave areexpressed by the following equations (46):

Xave=(ΣXi′)/(43×43)

Yave=(ΣYi′)/(43×43)

Zave=(ΣZi′)/(43×43)  (46)

where Σ means the sum of additions, with i′ ranging from 1 to 43×43.

(Stage four) Then, for individual colors Xi′ Yi′, Zi′, the distancesfrom the averages Xave, Yave, Zave are increased, with the respectiveaverages being taken as central values. The results of the processing,Xi″, Yi″ and Zi″, are expressed by the following equations (47):

Xi″=Xave×{(Xi′/Xave){circumflex over ( )}γ}

Yi″=Yave×((Yi′/Yave) {circumflex over ( )}γ}

Zi″=Zave×((Zi′/Zave) {circumflex over ( )}γ}  (47)

where i is between 1 and 43×43; γ is the degree of rosette emphasis; andsymbol “{circumflex over ( )}” represents a mathematical operation ofmultiplying a number by itself a certain number of times; hence,A{circumflex over ( )}γ signifies that A is raised to the γth power.

The degree of rosette emphasis γ is not limited to any particular value;it ranges typically from 0.8 to 3.0 and the greater its value, thesharper the rosette image that has been emphasized. However, if γ isincreased, the result of its application is frequently such that Xi″,Yi″, Zi″ will be converted to colors that are outside the range of colorreproduction on the proof printer. To avoid this problem, γ ispreferably adjusted to a smaller value if the desired color Xa isoutside or near the border of the range of color reproduction on theprinter. Whether the result of rosette emphasis by raising to the γthpower will be within or outside the range of color reproduction on theprinter can be verified by converting the tristimulus data XYZ todevice-dependent data RBG which are dependent on the proof printer.

Calculations are then performed to determine how many of the colors inthe 43×43 pixels deviate from the color reproduction range to whatextent and the degree of rosette emphasis γ is set to a new value suchthat the proportion of the deviating colors or the distance of deviationor the criterion for judgment based on these two factors will becomelower than a certain limit, thereby reducing the amount by which thecolor averaged for the 43×43 pixels departs from the desired color.

If the post-processing step consists of either color matching (S68) orrosette emphasis (S69) alone, the adjusted 6D-LUT can be used to producea color proof that features not only faithful reproduction of image finestructures such as moiré but also high precision in the adjustment ofcolors or rosette intensity. If the two post-processing steps S68 andS69 are performed in that order, each step once, not only is it possibleto achieve faithful reproduction of moiré but also high color precisionis ensured in addition to proper intensity adjustment of rosette.

If the two post-processing steps S68 and S69 are performed in thatorder, each step more than once, or if color-matching step S68 isrepeated more than once after steps S68 and S69 are performed in thatorder, each step once, colors that are indefinitely close to thoseappearing in printed documents can be reproduced.

If the thus obtained six-dimensional lookup table 6DLUT[i] [j] [k] [c][m] [y] is 6DLUT[1] [2] [3] [4] [5] [0], this entry of lookup table isloaded with tristimulus data XYZ of 400 dpi for a color that has imagecoordinates at (1,2) and which is characterized by 60% stage division ofK plate, 80% for Y plate, 100% for M plate and 0% for C plate.

From the definition of the rational number screen, a color with havingcoordinates (x,y)=(47,91) and K,C,M,Y=(20%, 20%, 20%, 20%) is the sameas the XYZ values recorded in the entry 6DLUT[4] [5] [2] [2] [2] [2].Due to the use of a rational number screen of the size 43×43, the samecolor appears in every 43 pixels, so the data for the fourth pixel isthe same as that for the 47th pixel. Generally speaking, data for apixel of (x,y)=(i0,j0) are the same as data for a pixel of(x,y)=(i0%m_(r), j0%n_(r)), where m_(r)×n_(r) is the size of a rationalnumber screen expressed in terms of the resolution of the output device.Sign “%” means a residue calculation (i.e., a mathematical operation foryielding the remainder after division).

Described above is the procedure for providing 6D-LUT for representativecolors before the production of actual color proofs is started. Byreferencing the thus provided 6D-LUT and performing interpolations,color proofs can be produced in a much shorter time than has beenrequired in the prior art.

While the specific process of conversion by making reference to the6D-LUT will be described below, it should be mentioned here that despitethe advantage of shortening the process (calculation) time, theapplication of the 6D-LUT has the side effect of reducing the contrastof moiré compared to the basic data processing technology which performsthe necessary mathematical operations on individual pixels. This has thepotential for the failure to achieve faithful reproduction of moiré andother image fine structures. In the basic data processing technology,calculations are performed at a very fine resolution (1600 dpi) whereasin the method of the present invention, calculations are only performedon the basis of the pixels in the original image (having a resolution of400 dpi) and the resulting loss of the high-frequency component of theinterference between the original image and pictures (which is the causeof moiré) is believed to be responsible for the lower contrast of moiré.

However, increasing the density of pixels under calculation to 1600 dpiis not consistent with the need to shorten the calculation time.Alternatively, filtering is applied to emphasize image fine structures,particularly moiré, and after selectively emphasizing the moirécomponent of the original image, 6D-LUT is preferably applied. Byadopting this compensation means, not only the first image finestructures such as moiré but also the second image fine structures suchas a rosette image can be reproduced faithfully.

We next describe the step of emphasizing the first image fine structuressuch as moiré (this step indicated by S59 in FIG. 16 is hereunderreferred to simply as “moiré emphasis step”). In the moiré emphasis stepS59, the dot area percentage data aj for the pixels in the plates (suchas CMYK) from the image document which have been subjected to colorconversion in step S52 are converted to dot area percentage data aj′which are emphasized in the first image fine structures such as moiréthat would appear in a printed image in a manner dependent upon thecombination of the screen ruling, screen angle and pictures or the likein the original image; the 6D-LUT which is the characterizing part ofthe second embodiment of the invention is then allowed to act on theresulting dot area percentage data aj′. For performing the conversionfrom aj to aj′, weights for the values of the pixel to be converted(which is hereunder also called “pixel under calculation”) andneighboring pixels are determined in a manner dependent on the period ofgrid units that is determined by the screen ruling LPI and the screenangle θ and the thus determined weights are adjusted by the coefficientof emphasis of moiré intensity α in such a way as to produce the firstimage fine structures such as moiré that occur at an intensityassociated with the type of the printing machine which is to output theprinted image, and the dot area percentage data aj are subjected toweighted averaging with the adjusted weights.

Therefore, moiré emphasis step S59 in the method of predicting andprocessing image fine structures according to the second embodiment ofthe present invention can be performed by applying the method ofpredicting and processing image fine structures according to the firstembodiment already described above. Briefly, the moiré emphasis step S59in the second embodiment can be performed in entirely the same manner asprocessing is effected in the image fine structure predicting andprocessing block 22 in the image fine structure predicting system 11shown in FIG. 1, except that the original image I and the areapercentage data with emphasized image fine structures I′ are replaced bydot area percentage data aj and the area percentage data with emphasizedimage fine structures aj′. Hence, the moiré emphasis step S59 in thesecond embodiment of the invention will not be described any further.

The dot area percentage data aj′ from step S59 which are the image datawith the moiré intensity adjusted by moiré emphasis in that step arethen sent to step S60, where they are processed with the 6D-LUT to beemphasized in the second image fine structures such as rosette.

We now describe the processing with 6D-LUT which is to be performed instep S60 (for rosette emphasis). As shown in FIG. 16, step S60 ofprocessing with 6D-LUT is the step of predicting (simulating) the secondimage fine structures in such a way that the dot area percentage dataa′j (j=CMYK) which have been adjusted in the first image fine structuressuch as moiré in the moiré emphasis step S59 are subjected tointerpolation with the 6D-LUT preliminarily provided in step S58,whereby they are converted to tristimulus data XYZ on a common colorspace which allow rosette and other second image fine structures to bereproduced and emphasized while leaving moiré and other first image finestructures intact as they were reproduced emphasized in step S59.Subscript j in a′j which represents the emphasized dot area percentagedata signifies CMYK and should be distinguished from the same characterused in expressing position coordinates (i,j). Hence, symbol “j_(a)” issubstituted in the following description.

The method of using the 6D-LUT is not limited to any particular type andas in the case where, in other fields, LUTs are used to produce imagesof the same appearance with different devices (e.g., a CRT-monitor and aprinter) or from different media (e.g., digital scanner data and printerdata), various techniques including volume interpolation, linearinterpolation and area interpolation may be employed. In the presentinvention, a four-dimensional space comprising the four colors (plates)CMYK is to be interpolated and this may be considered as an extension ofeither one of the following three cases: linear interpolation of aone-dimensional space; area interpolation of a two-dimensional space;and volume interpolation of a three-dimensional space. Hence, afour-dimensional quantity which is equivalent to the volume in athree-dimensional space is defined as a four-dimensional volume in afour-dimensional space. The following discussion assumes as arepresentative case the interpolation of a four-dimensional space usingthe four-dimensional volume.

The case to be discussed below is where the KYMC values at coordinates(i1,j1) are k, y, m and c, respectively. In this case, k is between k0and k1, y is between y0 and y1, m is between m0 and m1, and c is betweenc0 and c1; k0, y0, m0 and c0, as well as k1, y1, m1 and c1 are all suchthat they refer to either one of the N stages into which each of theplates KYMC is divided (in the case of division into 6 stages, eitherone of 0, 20, 40, 60, 80 and 100% is referred to); k1, y1, m1 and c1 areone stage higher than k0, y0, m0 and c0, respectively (take k as anexample; if k0 is 20%, k1 is 40% and this may be generally expressed bythe relation k0≦k≦k1).

Suppose here that the XYZ values corresponding to 6DLUT[i] [j] [k] [y][m] [c] are expressed by 6DLUT[i] [j] [k] [y] [m] [c].X, 6DLUT[i] [j][k] [y] [m] [c].Y and 6DLUT[i] [j] [k] [y] [m] [c].Z, respectively. Fromthe definition of a rational number screen, the XYZ values of theoriginal image at positional coordinates (i1,j1) are equal to the XYZvalues at (i2,j2) which satisfy the following equations (48):

i2=i1%m _(r) , j2=j1%n _(r)  (48)

where % designates a residue calculation; i2<m_(r) and j2<n_(r);m_(r)×n_(r) represents the size of the rational number screen. Hence, i2and j2 represent positional coordinates in the 6D-LUT.

If the results of determination by interpolation of 6D-LUT are writtenas 6DLUT(i,j,k,y,m,c).X−Z, they can be calculated by the followingequations (49):

6DLUT(i1,j1,k,y,m,c).X

=6DLUT(i2,j2,k,y,m,c).X

=6DLUT[i2] [j2] [k0] [y0] [m0] [c0].X*YV0000[i2] [j2]

+6DLUT[i2] [j2] [k0] [y0] [m0] [c1].X*YV0001[i2] [j2]

. . .

+6DLUT[i2] [j2] [k1] [y1] [ml] [c1].X*YV1111[i2] [j2]

=6DLUT[i2] [j2] [k0] [y0] [m0] [c0].X

*(k1−k)(y1−y)(m1−m)(c1−c)*(k1−k0)(y1−y0)(m1−m0)(c1−c0)

+6DLUT[i2] [j2] [k0] [y0] [m0] [c1].X

*(k1−k)(y1−y)(m1−m)(c−c0)*(k1−k0)(y1−y0)(m1−m0)(c1−c0)

. . .

+6DLUT[i2] [j2] [k1] [y1] [m1] [c1].X

*(k−k0)(y−y0)(m−m0)(c−c0)*(k1−k0)(y1−y0)(m1−m0)(c1−c0)

=6DLUT[i2] [j2] [k0] [y0] [m0] [c0].X

*(k1−k)(y1−y)(m1−m)(c1−c)*V

+6DLUT[i2] [j2] [k0] [y0] [m0] [c1].X

*(k1−k)(y1−y)(m1−m)(c−c0)*V

. . .

=6DLUT[i2] [j2] [k1] [y1] [m1] [c1].X

*(k−k0)(y−y0)(m−m0)(c−c0)*V

6DLUT(i1,j1,k,y,m,c).Y

=6DLUT[i2] [j2] [k0] [y0] [m0] [c0].Y

*(k1−k)(y1−y)(m1−m)(c1−c)*V

+6DLUT[i2] [j2] [k0] [y0] [m0] [c1].Y

*(k1−k)(y1−y)(m1−m)(c−c0)*V

. . .

+6DLUT[i2] [j2] [k1] [y1] [m1] [c1].Y

*(k−k0)(y−y0)(m−m0)(c−c0)*V

6DLUT(i1,j1,k,y,m,c).Z

=6DLUT[i2] [j2] [k0] [y0] [m0] [c0].Z

*(k1−k)(y1−y)(m1−m)(c1−c)*V

+6DLUT[i2] [j2] [k0] [y0] [m0] [c1].Z

*(k1−k)(y1−y)(m1−m)(c−c0)*V

. . .

+6DLUT[i2] [j2] [k1] [y1] [m1] [c1].Z

*(k−k0)(y−y0)(m−m0)(c−c0)*V  (49)

where i2=i1%m_(r) and j2=j1%n_(r); YVxxxx represents thefour-dimensional volume of a four-dimensional grid unit (afour-dimensional cube defined as an equivalent of a cube in athree-dimensional space) which includes vertices (kw,yw,mw,cw) [whichare diagonally opposite to (kx,yx,mx,cx)] and a point (k,y,m,c);V=(k1−k0)(y1−y0) (m1−m0) (c1−c0).

It should also be noted that each of x'es is YVxxxx is either 0 or 1 andthat each of w's in kw, yw, mw and cw is also either 0 or 1, providedthat when x is 0, w is 1 and vice versa.

Thus, using (referencing) the 6D-LUT 77, image data (dot area percentagedata aj_(a)′) which have positional coordinates of (i1,j1) in theoriginal image can be processed to yield tristimulus data XYZ in a CIEXYZ color system in a common color space which have the first image finestructures such as moiré and the second image fine structures such asrosette emphasized and adjusted in accordance with the type of theprinting machine to be eventually used to produce a printed document. Bydetermining the tristimulus data XYZ for all of the positionalcoordinates in the original image, full-image tristimulus data XYZ canbe obtained. The thus obtained full-image tristimulus data XYZ are sentto DP 53, where they are subjected to the next step S61 for performingcolor conversion in a device-dependent manner.

We next describe the color conversion and color proof production whichare to be performed by DP 53 in step S61.

In the color conversion process of step S61, the tristimulus data XYZ ina common color space which result from the processing with 6D-LUT instep S60 and which have image fine structures such as moiré and rosetteemphasized properly are converted to gradation image data which aredependent on device such as DP 53. The color space represented by thedata to be converted was already determined at the point of time when6D-LUT was constructed in step S58 and, in the case under consideration,said data are represented by tristimulus values in a CIE XYZ colorsystem. Therefore, in order to output matching colors on a proofprinter, it is necessary to perform data conversion to those valueswhich are dependent on the output device (e.g. a continuous-tone colorprinter or a display such as CRT), as exemplified by device-dependentRGB. To determine RGB image data for outputting a color proof on DP 53,interpolation is performed in the color conversion process using colorconverting LUTs (lookup tables) capable of conversion from thetristimulus data XYZ to device-dependent RGB data.

The thus obtained RGB image data are used by DP 53 to output a colorproof. The image on the output hard copy, namely, the color proof CPbfeatures not only color matching with the color printed document 62 butalso faithful reproduction of peculiar image fine structures which wouldappear in halftone images. In other words, moiré, rosette and otherimage fine structures (peculiar patterns) that are substantially thesame as those which would appear in the color printed document 62 can befaithfully reproduced on the color proof CPb.

Described above are the basic compositions of the method of predictingand processing image fine structures according to the second embodimentof the invention, as well as the color print producing system whichimplements this method.

In the above-described exemplary case of 6D-LUT generation in step S58,colorimetric data Xi, Yi, Zi are used to construct the first tristimulusdata X, Y, Z and the second tristimulus data X′, Y′, Z′, both kinds ofdata being mean colorimetric data. Alternatively, other common colorspace data (device-independent data) such as chromaticity or gray scaledata may be used to construct the mean colorimetric data. All that isrequired is that when image data such as dot area percentage data areconverted to image data with emphasized image fine structures such asrosette using the 6D-LUT in step S60, such image data should beconstructed as mean colorimetric data on a common color space using datathat were measured on a non-device dependent color space (i.e. commoncolor space). Speaking of the dot arrangement in the threshold matrices64 and 74, so-called FM screens characterized by a random arrangement ofhalftone dots may equally be employed to produce color proofs byimplementation of the present invention.

As already described with reference to FIG. 18, the first tristimulusdata X, Y, Z determined in the process of rendering into bit map datab′j (also called “halftoning”) in step S63 and subsequent dataprocessing in step S64 are composed of pixels which are several timessmaller than pixels at a resolution of 400 dpi (=Re3 or the resolutionof DP 53); for example, the pixels comprising the first tristimulus dataX, Y, Z have an-intermediate resolution (Re5=1600 dpi). The process ofdetermining the first tristimulus data X, Y, Z is as follows: first, apixel of an intermediate resolution is divided into a matrix of 28×28dots of a much smaller area [one dot having a resolution of 44800 dpi(=Re4)] (see FIGS. 21A and 21B); check is made as to which one of the 16primary colors is represented by each of said tiny areas and the dotsrepresenting the 16 primary colors are summed up for the individualpixels having the intermediate resolution (the count-up step S65);preliminarily measured tristimulus data Xi, Yi, Zi for the 16 primariesare subjected to weighted averaging with weights which represent thecounts of the 16 primaries. If the area percentage ci of each of the 16primary colors is written as Pi (existential probability), the firsttristimulus data X, Y, Z can be determined by the following equation(50):

X=Σ(Pi×Xi)(i=1−16)  (50)

which also holds for Y and Z.

As described in connection with the comparing step S63, the colorrepresented by each small-area dot can be readily determined bycomparing the threshold values of the threshold matrices 74 with thevalues of CMYK dot area percentage data aj (dot percentage or dot areapercentage). On the other hand, in order to ensure high precision in thecolors of pixels at the intermediate resolution, the number of smallareas has to be increased to, for example, as many as 28×28 dots andthis only results in prolonged calculations.

To deal with this problem, the present inventors already proposed aconvenient technique using cumulative histograms in Japanese PatentApplication Hei 8-179122. According to this technique, the existentialprobability (also called “dot presence probability”) Pi is determinedfor each of the pixels at the intermediate resolution by a stochasticmethod of approximation such as the aforementioned Neugebauer'sequation. Briefly, Pi (i=1−16) is determined in accordance with thefollowing equations (51):

P1=(1−Pc)(1−Pm)(1−Py)(1−Pk)

P2=Pc·(1−Pm)(1−Py)(1−Pk)

P3=(1−Pc)·Pm·(1−Py)(1−Pk)

P4=Pc·Pm·(1−Py)(1−Pk)

P5=(1−Pc)(1−Pm)·Py·(1−Pk)

P6=Pc·(1−Pm)·Py·(1−Pk)

P7=(1−Pc)·Pm·Py·(1−Pk)

P8=Pc·Pm·Py·(1−Pk)

P9=(1−Pc)(1−Pm)(1−Py)·Pk

P10=Pc·(1−Pm)(1−Py)·Pk

P11=(1−Pc)·Pm·(1−Py)·Pk

P12=Pc·Pm·(1−Py)·Pk

P13=(1−Pc)(1−Pm)·Py·Pk

P14=Pc·(1−Pm)·Py·Pk

P15=(1−Pc)·Pm·Py·Pk

P16=Pc·Pm·Py·Pk  (51)

The existential probabilities Pc, Pm, Py and Pk for the colors C, M, Yand K, respectively, are orthodoxically determined by comparing the dotarea percentage data aj with the threshold values T of the thresholdmatrices 74. However, considering that the value C (or M, Y or K) of thedot area percentage data aj is constant at the level of intermediateresolution whereas the threshold value T varies with the position of aspecific tiny area, the process of comparison in step S63 may beaccelerated by performing it on each group of pixels at the intermediateresolution. To this end, a cumulative histogram of threshold values Tmay be constructed for each number of tiny areas composing one pixel atthe intermediate resolution, namely, for each matrix of 28×28 dots(28×28 threshold values). The vertical axis of each cumulative histogramplots cumulative frequency, which corresponds to the existentialprobability Pi.

There may sometimes be the need to reproduce various aspects of thesensation in quality of the print sheet on a color proof and theyinclude the following: 1) “unevenness” which is felt as a random changein density pattern that occurs in the image reproduced on the printsheet by ink transfer from the uniform image on press plates; 2)“graininess” which is also felt as a random change in density patternbut which is fine enough to introduce “jaggies” into the edges of areproduced image (at shorter periods than “unevenness”); 3) “texture”which is felt as a peculiar density pattern depending on the type of theprint sheet. There may also be the need to correct color shifts andother defects in the simulation of image fine structures. In a case likethese, the methods described in commonly assigned Unexamined PublishedJapanese Patent Applications (kokai) Hei 9-115854 and Hei 9-270930 mayof course be applied either prior to or after the processes ofpredicting (or simulating) image fine structures in accordance with theinvention, such as moiré emphasis in step S59 or rosette emphasis instep S60.

It should also be noted that the adjustment of the intensities of moiréand sharpness emphasis which are preferably added to the moiré emphasisstep S59 is by no means limited to the foregoing example which has beendescribed as a specific case of the first embodiment of the invention.

For actual application of the invention method of predicting andprocessing image fine structures, a printed document having a screenruling of 175 and screen angles of 15, 45, 0 and 75 degrees for CMYK wassimulated in accordance with the color proof producing system 60 shownin FIG. 16 to output a color proof CPb on “Pictrography 3000” of FujiPhoto Film Co., Ltd. which was a photographic continuous-tone digitalprinter of 400 dpi. In the experiment, halftones were expressed by therepetition of patterns in a single cell of a size 43×43 (a square about2.73 mm per side on Pictography) and the degree of moiré emphasis α, theratio of sharpness emphasis β and the ratio of rosette emphasis γ wereset at 1.2, 0.5 and 2.0, respectively (except that for the colors thatwere close to the bounds of the range of color reproduction by thedigital printer “Pictrography 3000”, γ was adjusted to a value close to0.0).

For comparison, a color proof CPa was produced on the same digitalprinter “Pictrography 3000” using the conventional color proof producingsystem shown in FIG. 23.

The image on the color proof CPb was compared with the image formed on aprint sheet using an actual color printing machine and with the image onthe color proof CPa produced by application of the conventional colorproof system. The image on CPb was capable of faithful reproduction ofmoiré, rosette image and other peculiar patterns that would appear inthe image on the color printed document 62 output from the colorprinting machine. As regards “graininess” and “unevenness”, the image onCPb was closer to the image on the printed document 62 than the image onCPa which was produced with the digital printer using the Naugebauer'sequation according to the conventional color proof system. Thus, theoverall result of simulation by the method of the invention was quitesatisfactory.

When the mathematical operations involved in the method of the inventionwere performed with a personal computer “Power Macintosh 9500/132” ofApple Computer, Inc., it took only 10 minutes to complete calculationper proof of size A4, which was several to less than a hundred timesshorter than required in the prior art. Thus, substantial reduction inthe calculation time was realized by the invention.

In the foregoing example, a continuous-tone printer is used as the imageoutput device but this is not the sole case of the invention and itsconcept is also applicable to a display monitor and so forth.

It has been commonly held that color printers and the like which havelow resolution on the order of 400 dpi are incapable of reproducingpeculiar patterns that will appear in color printed documents at higherresolution on the order of 2000 dpi. According to the second embodimentof the invention, the Naugebauer's equation is not used in generatingdevice-independent data for producing printing proofs from an imageoutput device but instead threshold matrices for a printing screen arerendered into bit map data of high resolution to determine the areapercentage for each of the colors to be reproduced and the thusdetermined values of dot percentage are used as weight coefficients forcolorimetric data in computing mean colorimetric data at low resolutionby referencing position-dependent multi-dimensional lookup tables suchas five- or six-dimensional lookup tables having two position parametersand three or four color parameters. Because of these features, themethod of the invention can be implemented with an image output devicesuch as a convenient and consistent-performance continuous-tone printer,for example, a color printer of low resolution and yet peculiarphenomena that occur on account of the halftone dots in a printeddocument can be simulated as faithfully as can be achieved by means ofexpensive dot-forming proofers. If the data for producing printingproofs as generated by the method of the invention are supplied into theimage output device such as a color printer, the latter will output acolor proof carrying an image that provides an accurate, faithful andrapidly accessible reproduction of moiré, rosette and other image finestructures peculiar to the color printed document.

In addition, the method of the invention realizes substantial reductionin the length of time required by mathematical operations to beperformed in simulating image fine structures.

The method of the invention also provides an improvement in theprecision of color reproduction, thereby producing a proof representingcolors adequately closer to those in a printed document.

A further advantage of the method of the invention is that the amplitudeof simulated second image fine structures such as rosette can beenhanced without causing any side effects such as deterioration in moiréand other first image fine structures and the lowering of colorprecision.

Yet another advantage of the method of the invention is that theamplitude of simulated second image fine structures such as rosette canbe adjusted by optimization for the specific type of a printing machineor medium to be used.

According to still another advantage of the method of the invention,moiré, line discontinuities and other image fine structures can bepredicted on either a hard or soft proof accurately, easily and within ashort time, preferably without impairing the image sharpness.

The present invention also enables the intensities of moiré andsharpness to be adjusted on simulations (proofs) of image finestructures and, therefore, in addition to the simulation of image finestructures such as rosette, image sharpness and moiré can be simulatedin a manner appropriate for the specific type of the printing machine tobe used, thereby enabling the production of proofs with higher fidelity.

According to a further advantage of the invention, lookup tables areconstructed by such a method that mean colorimetric data are processednot only for color correction but also from the center outward, withmean predicted colorimetric data located in the center and this allowsthe reproduction of nearly pure colors, which eventually results in evenmore faithful reproduction of image fine structures such as a rosettepattern.

While the method of the invention for predicting and processing variousimage fine structures has been described above with reference to twospecific embodiments, it should be noted that the invention is by nomeans limited to those embodiments and various improvements and designmodifications are of course possible without departing from the spiritand scope of the invention.

What is claimed is:
 1. The method of predicting and processing imagefine structures, comprising: separating pixel values of an originalimage into pixels for each of CMY or CMYK plates; converting the pixelsseparated in said separating step to pixel values for predicting imagefine structures which will appear in a printed halftone image, bysubjecting the values of the pixel to be converted and neighboringpixels to weighted averaging with adjustable weights dependent on theperiod of grid units that is determined by the screen ruling and screenangle for said printed halftone image, wherein said pixel values forpredicting image fine structures which will appear in a printed halftoneimage is image data of a continuous tone image; wherein said weightsdependent on the period of grid units are arranged in the respectiveelements of a weighting matrix of the same mask size as said neighboringpixels, wherein if the weights to be arranged in the respective elementsof said weighting matrix are written as F(i, j, k, l), if E(k, l) iswritten for a coefficient matrix of the same size as said weightingmatrix, said coefficient matrix having a coefficient of 1.0 at thecenter and a coefficient of 0 in the other positions and if thecoefficient of said weight adjustment is written as α, then the weightto be arranged in the respective elements of said weighting matrix afterweight adjustment are given by the following equation (1):F′(i,j,k,l)=α{−E(k,l)+F(i,j,k,l)}+E(k,l)  (1) where (i, j) representsthe coordinates of the position at which said pixel to be converted islocated in said original image, and (k,l) represents the coordinates inthe weighting matrix of the same mask size as the pixel to be convertedand said neighboring pixels.
 2. The method according to claim 1, whereinsaid coefficient of weight adjustment α is adjustable within a range of0.6-1.8.
 3. The method according to claim 1, wherein the weights to bearranged in the respective elements of said weighting matrix aredirectly selected from among the threshold values that are arranged in ahalftoning threshold matrix and which are located in positions thatcorrespond to those pixels of which the values are to be converted. 4.The method according to claim 5, wherein said direct selection fromamong the threshold values means the use of either any one of saidthreshold values or the average of said threshold values.
 5. The methodaccording to claim 1, wherein the weights to be arranged in therespective elements of said weighting matrix are the values of therespective elements of the weighting matrix which are directly selectedfrom among the threshold values that are arranged in a halftoningthreshold matrix and which are located in positions that correspond tothose pixels of which the values are to be converted, said values of therespective elements of the weighting matrix having been processed with adecreasing filter the value of which decreases gradually from thecentral element of the weighting matrix towards peripheral elements. 6.The method according to claim 1, wherein the weights to be arranged inthe respective elements of said weighting matrix are the values of therespective elements of the weighting matrix which are directly selectedfrom among the threshold values that are arranged in a halftoningthreshold matrix and which are located in positions that correspond tothose pixels of which the values are to be converted, said values of therespective elements of the weighting matrix having been transformed by afunction such as to provide a spatial distribution of increasedgradient, and the transformed values having been subsequently processedwith a decreasing filter the value of which decreases gradually from thecentral element towards peripheral elements.
 7. The method according toclaim 5, wherein the pixel values for predicting said image finestructures to which the pixel values of said original image have beenconverted are those having the resolution of a device for outputting theprinted halftone image, said halftoning threshold matrix being onehaving a higher resolution than said output device and changed to theresolution of said output device as the processing with said decreasingfilter is performed.
 8. The method according to claim 1, wherein saidneighboring pixels delineate a rectangle.
 9. The method according toclaim 1, wherein the pixel values subjected to weighted averaging withsaid adjusted weights are obtained by internally or externally dividingnot only the pixel values subjected to weighted averaging with theweights dependent on the period of said grid units but also the valuesof said pixels to be converted.
 10. The method according to claim 1,wherein said original image includes four edge portions, wherein pixelscomprising said four edge portions are not subject to weighting.
 11. Themethod according to claim 1, wherein said neighboring pixels physicallyborder said pixel to be converted.
 12. The method according to claim 1,wherein said neighboring pixels physically border said pixel to beconverted; wherein said pixel to be converted and neighboring pixelscorrespond to selected pixels of said original image, and said weightedaveraging uses values of said neighboring pixels.
 13. The method ofpredicting and processing image fine structures, comprising: separatingpixel values of an original image into pixels for each of CMY or CMYKplates; converting the pixels separated in said separating step to pixelvalues for predicting image fine structures which will appear in aprinted halftone image, by subjecting the values of the pixel to beconverted and neighboring pixels to weighted averaging with adjustableweights dependent on the period of grid units that is determined by thescreen ruling and screen angle for said printed halftone image, whereinsaid pixel values for predicting image fine structures which will appearin a printed halftone image is image data of a continuous tone image;wherein said weights dependent on the period of grid units are arrangedin the respective elements of a weighting matrix of the same mask sizeas said neighboring pixels; wherein the weights to be arranged in therespective elements of said weighting matrix are the values of therespective elements of the weighting matrix which are directly selectedfrom among the threshold values that are arranged in a halftoningthreshold matrix and which are located in positions that correspond tothose pixels of which the values are to be converted, said values of therespective elements of the weighting matrix having been processed with adecreasing filter the value of which decreases gradually from thecentral element of the weighting matrix towards peripheral elements;wherein the weights to be arranged in the respective elements of saidweighting matrix are either the values obtained by the processing withsaid decreasing filter or the respective values of the threshold matrixas changed to the resolution of said output device and which have beensubjected to processing with a sharpness filter having characteristicssubstantially opposite to those of the processing with said decreasingfilter.
 14. The method according to claim 13, wherein the ratio ofadjustment of the sharpness of said sharpness filter is adjustable. 15.The method according to claim 14, wherein if the elements of saidsharpness filter are written as U(k,l), if E(k,l) is written for acoefficient matrix of the same size as said sharpness filter, saidcoefficient matrix having a coefficient of 1.0 at the center and acoefficient of 0 in the other positions and if the ratio of saidsharpness adjustment is written as β, then the elements of saidsharpness filter after sharpness adjustment are given by the followingequation (2): U′(k,l)=β{−E(k,l)+U(k,l)}+E(k,l)  (2) where (k,l)represents the coordinates in said sharpness filter.
 16. The methodaccording to claim 13, wherein said decreasing filter and said sharpnessfilter are combined into a single filter.
 17. The method according toclaim 14, wherein said weighting matrix after said weight adjustment andsaid sharpness filter after said sharpness adjustment are assembled intoa single filter.